Bibliographic record
Abstract
\n A longline ‘métier’ using small hooks for ‘red’ sea breams (Pagellus acarne and Pagellus erythrinus) in the Algarve (south of Portugal) was studied. Experimental longlining was carried out with three sizes of "Mustad" round bend, flatted, spade end Quality 2316 DT hooks (numbers 11, 13 and 15) and two types of bait: razor shell (Ensis siliqua) and mud shrimp (Upogebia pusilla). A total of 3 328 fish and at least 36 species were caught with 33 600 hooks fished in 28 longline sets. Five species of sea breams (Sparidae) accounted for 79% of the catch: Pagellus acarne, Pagellus erythrinus, Diplodus vulgaris, Spondyliosoma cantharus, and Boops boops. High catch rates of 20-30 fish per 100 hooks were made in a number of 1 200 hook longline sets, with total catch weights of 40 to more than 60 kg per set. In general, the smallest hook (number 15) had the highest catch rate. Bait type did not significantly affect the catch size distributions. Although more fish were caught with the razor shell bait, higher catch rates of ‘red’ sea breams were obtained with mud shrimp. Catch rates were also affected by the location of the fishing grounds and the time of the set, with the highest catch rates obtained when the longline was set within two hours before sunrise. A wide size range was caught for each species, with highly overlapped catch size frequency distributions for the three hook sizes used. Except for Spondyliosoma cantharus, few illegal-sized fish were caught, even with the smallest hook. The logistic model fitted by maximum likelihood was used to describe hook selectivity for Diplodus vulgaris, Pagellus acarne, Pagellus erythrinus, and Spondyliosoma cantharus.\n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.986 | 0.990 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".