Comparative Study of the Intensity of Feeding of Cod (Gadus morhua) off Newfoundland and of the
Bibliographic record
Abstract
In stocks experiencing intensive fisheries, the quantitative assessment of food con-sumption is important in understanding the trophic interrelationships. As an example it has been reported that exploitation of the planktophage capelin stock in the Barents Sea resulted in the loss of the stable food base of the Arcto-Norwegian cod. The period of the sharpest decline in capelin abundance (1987-88) was followed by transition of food consumption of cod to the young of commercial fish and other small non-commercial fish and crustaceans. Consequently, changes were noted in some biological conditions of cod (Orlova et al., 1990a). Food consumption investigations formed the basis for the construction of multi-type models in this study. The Arcto-Norwegian cod distribution is close to that of the Newfound-land cod, and hence similarities in rate of growth and sexual maturing, and also hydrological conditions and food composition, with the exception of sand eel (Ammodytes sp.), are known (Popova, 1962; Turuk, 1973, 1976; Lilly, 1987). This permitted the comparison of the intensity of feeding of cod of both stocks. A comparative analysis of the results of calculations of daily
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".