Local, Seasonal, and Yearly Condition of Juvenile Greenland Halibut Revealed by the Le Cren Condition Index
Bibliographic record
Abstract
Abstract An understanding of biological characteristics, such as growth patterns, condition, and energy reserves, is important for better understanding the environmental constraints exerted on fish populations. This is especially true for exploited fish stocks in the current context of climate change. Using biological data collected from 2006 to 2009 during bottom trawl research surveys by Fisheries and Oceans Canada in the estuary and Gulf of St. Lawrence (EGSL) as well as data from 2000 to 2018 in the northwest Atlantic, we sought to improve our knowledge on the seasonal condition of Greenland Halibut Reinhardtius hippoglossoides juveniles and to better understand the divergence in some life history traits between juveniles captured in these two regions. We validated the use of the Le Cren condition index and evaluated its relationship with energetic status in juvenile (20–32‐cm TL) Greenland Halibut. In the EGSL, juvenile condition was higher in winter and spring compared to summer and fall. Such variations may result from this species’ pelagic predation activity and prey availability. Juveniles captured in the EGSL during 2016–2017 were larger but had a lower condition index than those captured in the northwest Atlantic, but we found no indication of earlier sexual maturation in the EGSL that could explain the sex ratio differences we observed in catches from these two areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".