Predicting the recruitment strength of an annual squid stock: <i>Loligo gahi</i> around the Falkland Islands
Bibliographic record
Abstract
Two cohorts of Loligo gahi recruit to the fishery around the Falkland Islands, the first in summer and the second in autumn-winter. We investigated factors affecting recruitment to these cohorts using 13 years of data (1987-1999). The first evidence for density-dependent effects on recruitment in a squid population is reported, with very high spawning stock biomass leading to a reduction in recruitment in both cohorts. October sea surface temperature was negatively correlated with recruitment to the second cohort 6 months later, and a linear model explained 66% of the variance in recruitment strength. A model combining sea surface temperature and spawning stock size explained 77% of the variance. Thus, low October temperatures and moderate stock sizes lead to higher recruitment the following year than high October temperatures and high stock sizes. A strong negative relationship was also found between sea surface temperature in May and the timing of recruitment to the first cohort the following January-February, suggesting that higher temperatures lead to faster development of embryos or paralarvae and earlier recruitment to the fishery. A predictive model of recruitment size and timing should enable better management of L. gahi.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".