Taxonomic and Genetic Diversity of Decapods in Northeast Pacific, Canadian Arctic and Northwest Atlantic
Bibliographic record
Abstract
An exploratory project on the taxonomic and genetic diversity of decapods in three ocean subregions (Northeast Pacific, Canadian Arctic, and Northwest Atlantic), which were sampled in 2022, was undertaken by the Arctic Working Group under the Canada-U.S. Fisheries and Climate Collaboration between Fisheries and Oceans Canada (DFO) and the National Marine Fisheries Service (NMFS) of the National Oceanic and Atmospheric Administration (NOAA). This collaboration aims to pool Canadian and U.S. data to explore the impacts of broad-scale climate change on marine biodiversity. The present dataset includes 381 decapod species occurrences. DNA was extracted for a subset of 87 specimens (COI gene); sequences are publicly available on BOLD data portal under project code DDAO (https://portal.boldsystems.org/result?query=DDAO[recordsetcode]). // Un projet exploratoire sur la diversité taxonomique et génétique de décapodes échantillonnés en 2022 dans trois sous-régions océaniques (Pacifique Nord-Est, Arctique canadien et Atlantique Nord-Ouest) a été réalisé par le groupe de travail sur l'Arctique au sein de la collaboration canado-américaine sur les pêches et le climat entre Pêches et Océans Canada (MPO) et le National Marine Fisheries Service (NMFS) de la National Oceanic and Atmospheric Administration (NOAA). Ce cadre de collaboration vise à mettre en commun les données canadiennes et américaines afin d'étudier les effets du changement climatique à grande échelle sur la biodiversité marine. L'ensemble de données actuel comprend 381 occurrences d'espèces de décapodes. L'ADN a été extrait d'un sous-groupe de 87 spécimens (gène COI); les séquences sont accessibles publiquement sur la plateforme BOLD sous le code de projet DDAO (https://portal.boldsystems.org/result?query=DDAO[recordsetcode]).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".