Using caddisfly (Trichoptera) case-building behaviour in higher level phylogeny reconstruction
Bibliographic record
Abstract
Higher level phylogenetic analyses rarely include behavioural data, predominantly because such groups seldom have complex behaviours that are susceptible to analysis. Even when broad groups do share a complex behaviour, there is skepticism about the appropriateness of using behavioural traits in higher level phylogenetic analyses. The Integripalpia is a suborder of caddisflies and is an appropriate group to investigate the use of behaviour in higher level analyses because all larvae use a complex suite of behaviours to build portable cases. A thorough investigation of case-building in 10 families (19 exemplar genera) yielded 24 behavioural characters. A parsimony analysis produced 87 equally parsimonious trees (length = 56 steps, consistency index (CI) = 0.84, retention index (RI) = 0.88) that supported the monophyly of the integripalpian families, except for the Limnephilidae. Interfamilial relationships, although resolved, were not well supported with behaviour. Certain interfamilial relationships have also been difficult to establish reliably with morphological information, indicating a need for more characters (e.g., molecular) at this taxonomic level. This study indicates that if taxa share a complex behaviour (e.g., case building), then regardless of taxonomic level, one is likely to find shared derived behavioural characters that are useful for phylogeny reconstruction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".