Categories
Lucid Player v4

Lucid Player Why Discarded

During an identification session you may have an Entity in mind that the should ‘key out’, however the key discards the Entity. In this event you can use the ‘Why Discarded’ feature available on the main toolbar, or by right clicking on the discarded Entity within the Entities Discarded panel to show the context pop-up menu with the Why Discarded menu option. This will open the Why Discarded dialog and show which of your selected features didn’t match the Entity and caused it to be discarded.

Lucid Player Why Discarded dialog example
Lucid Player Why Discarded dialog example
Categories
Lucid Builder v4

Lucid Builder Score Analyser

Analysing a key’s scores

Once a key is partially scored, you may analyse the scoring using the Score Analyser. The Score Analyser provides two main types of analysis:

A Difference analysis is used to identify pairs or groups of Entities that a user of the key will have difficulty separating, because there are few differences in their scoring. Adding more Features that differ between such Entities would improve the key. While the Lucid Builder cannot tell you which Features to add, pointing out which Entities need more Features may help you improve the key.

A Polymorphism analysis identifies taxa that are more or less polymorphic in their scoring. A taxon is polymorphic when it is scored for two or more States of a Feature. Taxa that are highly polymorphic (I.e. frequently scored for two or more States per Feature) will be difficult to remove from Entities Remaining even when the particular set of States chosen does not occur in the Entity – that is, they may be falsely retained. Splitting highly polymorphic taxa into two or more less-polymorphic subtaxa will improve the efficiency of the key.

The Score Analyser is started by choosing the Score Analyser tab.

Lucid Builder Score Analyser tab
Lucid Builder Score Analyser tab

After analysis, choose Differences by Entity or Differences by group to perform a difference analysis, or Polymorphisms to produce a polymorphism analysis.

Difference Analysis

The Score Analyser displays a bar graph showing the number of differences between all pairs of Entities.

Lucid Builder Score Analyser example
Lucid Builder Score Analyser example

On the left of the graph are pairs of Entities that differ by relatively few Features; on the right of the graph are pairs of Entities that differ by many Features.

The slider on the bar graph is used to find those pairs of Entities that differ by less than a nominated number of features. Move the slider to a position on the left tail of the graph, and the middle panel of the Score Analyser will display all pairs of Entities that differ by less than the nominated number of features. Selecting an entity in the middle panel will display the Features that differ.

If Differences by Entity is selected, the middle panel will list Entities in order of each Entity. If Differences by groups is selected, the lower panel will display Entities that form mutually difficult groups.

Use Save to File to save the results of the lowermost panels to a text file.

Polymorphism Analysis

The Score Analyser displays a bar graph showing the distribution of polymorphisms for the Entities in the key. The polymorphism score of an Entity is a percentage of the total number of States scored non-absent for the Entity divided by the total number of States in the key.  

Lucid Builder Score Analyser Polymorphisms example
Lucid Builder Score Analyser Polymorphisms example

The slider on the bar graph is used to find those entities that are highly polymorphic. Move the slider to a position on the right tail of the graph, and the middle panel of the Score Analyser will display all Entities with a polymorphism value greater than the nominated value. Selecting an Entity in the middle panel will display the features that are scored polymorphically, with their scored States.

Use Save button to save the results of the lowermost panels to a text file.

Categories
Lucid Builder v4

Lucid Builder About Lucid scores

Applies to Matrix key projects

Absent score icon Absent

The Lucid Absent score is used to record that a state of a feature does not occur in an entity. This should be a definitive statement: if you are uncertain whether a state does or does not occur, it is safer to use the Uncertain uncertain score icon score.

Absent is, in some ways, the most important score in Lucid. If an entity is scored as Absent for a state and a user of the Lucid Player chooses the state, the entity will be discarded from Entities Remaining into Entities Discarded, and hence will be removed from contention for the identification. A false Absent is a more serious error in a key than a false Present, since it will cause an entity to be falsely removed from Entities Remaining, and hence may preclude a correct identification.

Absent is the default score given to all entities for all states, until another score is given.

For numeric features, Absent is the default score if no numeric record has been entered for the entity. See the topic Scoring numeric features for more details.

common score icon Common

Common is the normal score given in Lucid when a state occurs in an entity. For example, if a species of plant always or usually has blue flowers, the state blue of the feature Flower colour would be scored using the Common score.

Common may be used in concert with other scores. For example, if a species of plant usually has blue flowers but occasionally has red flowers, then the state blue would be scored Common and the state red would be scored Rare.

For numeric features, Common and Rare values are coded by entering ranges of numbers in the Numeric Coding dialog box. See the topic Scoring numeric features  for more details.

rare score icon Rare

Rare is used to record that a state occurs uncommonly or rarely in an entity.

Rare will usually be used in concert with other scores. For example, if a species of plant usually has blue flowers but occasionally has red flowers, then the state blue would be scored Common and the state red would be scored Rare.

Note that Rare should be used only in the case where a state occurs rarely in an entity, not in the case that the entity is rare. If a state always or usually occurs in a rare entity, you should still use the Common score.

In the Lucid Player, Rare is used to rank the list of Entities Remaining. If entity e is scored Rare for state s, then e will be moved down the list of Entities Remaining (List Mode) when the user chooses s (compared with other entities that are scored Common for s). Hence, if Rare is widely used in a key and at the end of an identification several entities remain, entities at the top of the list are more likely to be correct than entities at the end of the list.

For numeric features, Common and Rare values are coded by entering ranges of numbers in the Numeric Coding dialog box. See the topic Scoring numeric features for more details.

uncertain score icon Uncertain

Uncertain is used to record that the key author is uncertain or does not know whether a given state occurs or does not occur in a given entity.

Uncertain will sometimes be applied to all states of a feature and sometimes only to one or a few states of a feature. For example, consider a key to plants which includes a feature referring to the colour of the fruit. It may be that one species in the key is known only from a few specimens and fruits have never been observed. In that case, all the states of the Fruit colour feature would be scored Uncertain. Another species may be poorly known and yet it is clear that the fruits are not white or coloured, but they could be black or dark grey. In that case, white and the coloured states could be scored Absent and dark grey and black both scored Uncertain.

In the Lucid Player in normal identification mode, Uncertain is treated the same as Common. That is, if an entity e is scored Uncertain for state e will remain in Entities Remaining when a user chooses s. This is appropriate behaviour for an identification, as e should not be falsely discarded if a key user has more information about it than was available to the key developer.

The behaviour of the Player can also be changed by un-checking the Retain Uncertains menu option, in which case Uncertain is treated the same as Absent. This would be appropriate if a user of the key is querying the Player to return a list of all entities known to have state s (rather than all entities that may have s).

For numeric features, Uncertain is coded as a special value in the Numeric Coding dialog box. See the topic Scoring numeric features for more details.

common misinterpreted score icon  Present by Misinterpretation, and rare misinterpreted score icon Rarely Present by Misinterpretation

While the Uncertain score in Lucid is used to capture uncertainty by the key developer, the Present by Misinterpretation scores are used to preempt likely uncertainty or mistakes by a key user.

Consider a key to fish with a feature Number of dorsal fins and the states one dorsal fin and two dorsal fins. Some species of fish clearly have a single dorsal fin, while others clearly have two dorsal fins. Angler fish have two dorsal fins, but the first is modified into a fleshy lure that does not look like a normal fin. In this case a user of the key may misinterpret an angler fish as having a single dorsal fin. If angler fish were scored correctly as having two dorsal fins many users would make a mistake at this point. Conversely, scoring angler fish as having a single dorsal fin (to account for the likely mistake) would introduce erroneous coding into the key.

In Lucid the Present by Misinterpretation scores are used to account for likely user mistakes while maintaining the integrity of the data. In the case of angler fish, the state two dorsal fins would be scored Present, and the state one dorsal fin would be scored Present by misinterpretation.

Lucid Builder Present by Misinterpretation score example
Lucid Builder Present by Misinterpretation score example

In the Lucid Player in normal identification mode, Present by Misinterpretation is treated the same as Common. That is, if an entity e is scored Present by Misinterpretation for state s, then e will remain in Entities Remaining when a user chooses s. This is appropriate behaviour for an identification, as it pre-empts e being discarded when a user makes a common and predictable mistake.

The behaviour of the Player can also be changed by un-checking the Allow Misinterpretations menu option, in which case Present by Misinterpretation is treated the same as Absent. This would be appropriate if a user of the key is querying the Player to return a list of all entities that truly have one dorsal fin (rather than all entities that may appear to have one dorsal fin).

The Rarely Present by Misinterpretation score is used to encode a case where it is occasionally the case that a state could be misinterpreted as being present. For example, a species of fish may normally clearly have two dorsal fins, but in occasional specimens one dorsal fin may be reduced to a small spine that could be overlooked. In the Lucid Player with Allow Misinterpretations check-marked, a Rarely Present by Misinterpretation score is treated the same as a Rare score; with Allow Misinterpretations unchecked, Rarely Present by Misinterpretation is treated the same as Absent.

Lucid Builder Present by Misinterpretation with other another score example
Lucid Builder Present by Misinterpretation with other another score example

Note that the Present by Misinterpretation scores are usually used in concert with other scores, as in the case above. Sometimes, however, Present by Misinterpretation or Rarely Present by Misinterpretation may be used alone, especially when a feature is controlled by a dependency. For example, consider a key to plants with the features Petals (with states present and absent) and Petal colour (with states white and blue). A species of plant in the key may lack petals but have white, petal-like bracts around the flower which could be misinterpreted as petals. This species would be coded as Common for the state Petals: absentPresent by misinterpretation for the state Petals: present and Present by misinterpretation for the state Petal colour: white.

For numeric features, the By Misinterpretation score is set as an annotation for a range of numbers in the Numeric Coding dialog box. See the topic Scoring numeric features for more details.

not scoped score icon Not Scoped

The Not Scoped score in Lucid is used to enable coding of features that are useful and applicable only for a subset of the entities in the key. In the Lucid Player, features scored using Not Scoped are initially hidden, and are inserted into the Features Available list only when they become applicable to the identification.

Consider a large key with 1000 Entities. It will be appropriate to score some features for all the entities, using the normal Lucid scores (AbsentCommonRare etc.). But consider a feature that is useful for identification only in a small subgroup of the entities, and which is inapplicable, ambiguous or insufficiently known for the bulk of entities. In Lucid, the entities in the subgroup can be coded for the feature, but all the other entities (not in the subgroup) will be given the Not Scoped score for that feature. In the Lucid Player, the feature will only become available when the list of Entities Remaining includes only entities that are scored for the feature using normal scores.

To explain the Not Scoped score further, consider a key to fishes with the following Entities and Features:

Features

Entities

Body shape in cross-section

          Rounded

          Laterally flattened

          Dorsoventrally flattened

Paired spots along flank

          Adjacent, forming figures of eight

          Separated by a thin line

Barracuda

Bluefin Tuna

Red Gurnard

Sand Mullet

Spotted Weedfish

Crested Weedfish

…etc*

*The key also includes many other species of fishes.

The feature Body shape in cross-section can be scored for all the fish in the key, and it would be good practice to do so. The feature Paired spots along flank, however, is very useful to distinguish the two species of weedfish, which are otherwise difficult to separate, but is difficult to answer unambiguously for most other species of fish (some other species may also have paired spots in various arrangements on the flank, but for these other species the pattern of spots is not a useful feature for identification).

In this key, it may be important to be able to make use of the feature Paired spots along flank to separate the weedfish, but it would be difficult or impossible to score it for all fish. The Not Scoped score would be used in this circumstance. For all species other than the weedfish, both states of Paired spots along the flank are given the score Not Scoped, while for the weedfish the feature is scored using the normal Lucid scores. The figure below shows the coding for two species as it would appear in the Lucid Builder

Lucid Builder Not Scoped scoring example
Lucid Builder Not Scoped scoring example

In the Lucid Player, when the key is started the feature Paired spots along flank is initially hidden. As a user of the key addresses the available features, the list of entities becomes progressively reduced. It may be that, at some point in the identification, only weedfish remain in Entities Remaining. All the weedfish are scored for the feature Paired spots along the flank, so at this point the feature is inserted into Features Available and can be used to help discriminate the species of weedfish.

For multi-state features, the Not Scoped score is automatically assigned to all the states of a feature – it cannot be given to a single state.