Geometric morphometrics analysis: a complement to the revision of whelk taxonomy in the Arctic
Bibliographic record
Abstract
Ice cover, food availability, light intensity, limited dispersal capacity, presence of predators and reproduction mode are just a few of the drivers that influence benthic community dynamics, especially in the Arctic. Benthic organisms can respond to such drivers through morphologic variations, referred to as phenotypic plasticity. These variations are however hard to observe on Arctic whelk (genus Buccinum) and their diversity and extensive distribution increase the complexity of their identification. While genetics analyses can address this problem, they are not broadly accessible. More accessible are landmark-based geometric morphometrics which analytically identifies morphologic variations. This technique aims at identifying shape variations and could be used to identify intra- and inter-species morphologic variability in the genus Buccinum. The main objective of this project is to verify whether landmark-based geometric morphometrics, particularly 3-dimensional, could be used on whelk species. If this proof of concept proves successful, the next step will be to identify new morphologic traits to differentiate species and compare this technique to genetics analyses on multiple Buccinum species. This project could allow researchers to efficiently differentiate between whelk species on the basis of morphologic traits rather than through more demanding genetics analyses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".