Pelagic Fish at the Barents Sea Polar Front in May 2022
Bibliographic record
Abstract
## Methods ### Study area This dataset is the result from sampling at 5 stations at the Polar Front in the western part of the Barents Sea. ### Time coverage The samples were collected between 20 May 2022 and 25 May 2022. ### Sampling 5 pelagic trawl samples were collected with a Harstad pelagic trawl, which has an effective height of 9-11 m and width of 10-12 m when towed at ca. 3 knots. The mesh size of the inner liner of the cod end was 10 mm. The pelagic trawl was towed at ca. 3 knots for 20-30 min and abundances were standardized by converting to catch per unit effort (expressed in kilograms per cubic meter). ### Sample analysis All organisms were identified to the nearest species or genus onboard. Throughout all stations, capelin had a large size distribution, so individuals similar in length were sorted into approximate size classes (small, medium, and large). The total number and weight of each species was recorded. For large catches, subsamples of 20-30 individuals were taken with representing length distributions of the catch. The standard length, height at the anus (up to the nearest 1 mm), and weight (up to the nearest 0.1 g) were measured for all specimens in the (sub)sample. ### Fish stomach content analysis The stomachs were isolated and immediately preserved in 70% ethanol. For each individual stomach, the level of fullness (from 0: empty, to 4: full), prey composition (the count and % volume each prey item takes up in the stomach), and the level of digestion for each prey item (from 1: newly eaten, to 5: digested or non-identifiable) were estimated and recorded.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.002 |
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".