Using Citizen Science to Improve Our Understanding of Northern Shortfin Squid (Illex illecebrosus) and Longfin Inshore Squid (Doryteuthis pealeii) Ecology and Fisheries off Atlantic Canada
Bibliographic record
Abstract
Northern shortfin squid (Illex illecebrosus) and longfin inshore squid (Doryteuthis pealeii) are fished commercially and recreationally off Atlantic Canada, but limited reporting, particularly from recreational fisheries, has left major gaps in our understanding of their ecology and fishery dynamics. Our research used three years of data collected through citizen science initiatives, field visits, and commercial index harvesters to provide much-needed descriptions of squid fisheries and basic ecological characteristics, with a particular focus on Newfoundland and Labrador (NL). We documented active recreational squid fisheries landing both species, including the first confirmed observations of longfin inshore squid in the NL fishery. Distinct regional patterns emerged. North Coast Region fishers tended to use different gear and practices, achieved higher catch-per-unit effort (CPUE), and caught larger squid compared to other NL regions. South Coast Region fishers more frequently caught longfin inshore squid, and although relatively low CPUE was reported, this region tended to have a more active recreational fishery. Our findings reveal previously unrecognized regional variation in squid fisheries and highlight the need for improved understanding of squid ecology and the impacts of the recreational and commercial fisheries on the resource off Atlantic Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".