A geometric morphometric approach to establish body-shape trait criteria for aquatic insects
Why this work is in the frame
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Bibliographic record
Abstract
Body shapes of aquatic insect larvae reflect phenotypic responses to complex environmental conditions and can be used to infer habitat properties and indicate natural and anthropogenic perturbations in river ecosystems. Investigation of relationships between body shape and physical-habitat characteristics has been restricted by a lack of an objective schema for quantitative characterization of body-shape variation. We present a functional ecological framework for body-shape classification based on defined criteria. We applied a geometric morphometric (GM) approach to the general classification of body shape in 4 morphologically diverse orders, Ephemeroptera (E), Plecoptera (P), Trichoptera (T), and Odonata (O) collected from 3 sites with contrasting hydrological and hydraulic characteristics. We describe a robust classification of body shapes for E, P, and O, which possess a compartmentalized body plan, and suggest a preliminary classification for T. We compared GM body shapes with body-shape trait states available in trait databases and found discordance between the 2 classifications. We explored the value of GM body shapes to describe taxon shape structure of reference sites and to detect variation reflecting physical properties of the sites. GM body-shape classes can augment the trait states already available and enhance inference regarding habitat status. Patterns in the shape strategies of aquatic insects, particularly EPO taxa, can be used to extrapolate shape information for other taxonomic groups. GM provides a stable shape classification that can contribute to the description of different ecological strategies of aquatic insects. Expanding the scope of shape information available for many taxonomic groups can improve our understanding of how organism phenotype relates to environmental conditions and supports traits-based assessment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it