DFO Quebec Region Biodiversity of the Snow Crab Trawl Survey in the Lower North Shore (2018, 2022, 2024)
Bibliographic record
Abstract
Occurrence data of biodiversity during the snow crab survey on the Lower North Shore of the Gulf of St. Lawrence between Havre-St-Pierre and Blanc-Sablon. A research survey on snow crab (Chionoecetes opilio) was conducted by DFO in 2018, 2022 and 2024. The main objective of this survey was to assess crab abundance and the diversity of benthic and demersal species associated with crab habitat, following a fixed station sampling design using a beam trawl. The data provided is a compilation by species (or taxon) and by station. The taxonomic and geographic validity of the records were verified. // Données d'occurrences de la biodiversité recueillies lors d'un relevé de recherche sur le crabe des neiges (Chionoecetes opilio) réalisé par le MPO en 2018, 2022 et 2024 sur la Basse-Côte-Nord au nord du golfe du Saint-Laurent, entre Havre-St-Pierre et Blanc-Sablon. L’objectif principal de ce relevé était d’évaluer l’abondance du crabe ainsi que la diversité des espèces benthiques et démersales associées à l’habitat du crabe selon un plan d'échantillonnage à stations fixes utilisant un chalut à perche (connu aussi sous le nom de chalut à bâton). Les données fournies constituent une compilation par espèce (ou taxon) et par station. La validité taxonomique et géographique des enregistrements a été vérifiée.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".