Detecting Intraspecific Character Displacement by Morphological Markers in Riverine-Dwelling Invertebrate Larvae: The Case Study of Head Shape Variability in Leuctra fusca (Plecoptera: Leuctridae)
Bibliographic record
Abstract
Since morphological markers are recognized as useful tools to evaluate events of anthropic disturbances, we performed a preliminary study on head shape variability in the riverine-dwelling Leuctra fusca larvae as early alarm systems in running waters. Particularly, heads of 32 larvae were collected from two localities of River Aniene (central Italy) and photographed for digitizing landmarks and semilandmarks. The Cartesian x-y coordinates of all points were firstly converted to shape coordinates by Procrustes superimposition, and then analyzed for exploring the full potential of the application of geometric morphometric techniques. Where the principal component analysis revealed a clear pattern of variation between the 2 sampling sites, the Procrustes ANOVA highlighted this variation as highly associated with fluctuating asymmetry, the latter being traditionally connected with developmental accidents due to environmental conditions. No directional asymmetry was observed. Finally we didn’t find any pattern of allometric variation in the studied structure. Our study indicates that further studies ought to be employed to use geometric morphometrics as a valid tool for detecting and describing morphological variation as biomarkers in invertebrate organism such as stoneflies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".