Noteworthy occurrences among six marine species documented with community engagement in the Canadian Arctic
Bibliographic record
Abstract
Abstract Arctic marine ecosystems are changing, one aspect of which appears to be distributional expansions of sub-arctic species. For Arctic marine systems, there is limited occurrence information for many species, especially those found in restricted habitats (e.g., ice-covered, far north, or deep-water). Increasing observations through on-going Fisheries and Oceans Canada (DFO) community-based monitoring programs (e.g., Arctic Coast, Cambridge Bay Arctic Char stock assessment, Arctic Salmon, and Kugluktuk coastal surveys), community observation networks, and local media have augmented opportunities to document new occurrences of marine fishes. Combined data from historical records and contemporary observations at the local scale can then delineate these among three types of occurrences: gradual distributional expansion, episodic vagrants, and rare endemics. Here we document nine occurrences of unusual sightings across six fish species (Pink Salmon Oncorhynchus gorbuscha, Bering Wolffish Anarhichas orientalis, Greenland Shark Somniosus microcephalus, Broad Whitefish Coregonus nasus, Banded Gunnel Pholis fasciata and Salmon Shark Lamna ditropis) from six northern Canadian communities and classify the nature of each observation as rare, vagrant, or expanding distributions. Uniting scientific and local observations represents a novel approach to monitor distributional changes suitable for a geographically large but sparsely populated area such as the Canadian Arctic. The new occurrences are important for discerning the potential effects of the presence of these species in Arctic ecosystems. These observations more broadly will build on our understanding of northern biodiversity change associated with warming Arctic environments.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 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".