Bibliographic record
Abstract
This is the OBIS extraction of the Ocean Tracking Network and Department of Fisheries and Oceans Canada (DFO) Scotian Shelf Snow Crab Tagging, consisting of the release tagging metadata, i.e. the location and date when the tagged animal was released, and summarized detection events of tagged individuals. If readers are interested in the source dataset they may also inquire with the project PIs as listed here or on the OTN web site (https://members.oceantrack.org/project?ccode=ZSC). Abstract:The snow crab (Chionoecetes opilio) is the third most important fishery species in terms of landing value in Nova Scotia. Based on fishery landings, snow crabs are thought to segregate by sex and size. The species is migratory, based on the seasonal disappearance and reappearance of crabs in different geographical areas, and limited tagging studies that have been conducted to date. However, the tagging studies necessary to document the scale and scope of such migrations have not yet been conducted. Information about the migratory movements of the species will be very important to managers as they determine quotas for fisheries in the different crab fishing zones (geographical management areas). At present, snow crab movements are too poorly understood to assist in developing management regimes for the species. This project involves the tagging and release of snow crab onto the Scotian Shelf by the Department of Fisheries and Oceans along with subsequent deployments of acoustic receivers in order to track their movements. Additional receivers have been deployed in the area through collaboration with industrial partners. Tagging has occurred in several areas off Cape Breton, Halifax and within St. Anne's Bank MPA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".