Indicators of abundance and spatial distribution of lobsters <i>(Homarus americanus)</i> from standard traps
Bibliographic record
Abstract
Abstract Indicators of abundance for American lobster (Homarus americanus) based on 8 years of trap catch rates (catch‐per‐unit‐effort, CPUE) were evaluated. Volunteer harvesters recorded count, sex and size of lobsters captured in standard traps on a daily basis during the fishing season in coastal Nova Scotia, Canada. We examined the extent to which standardised CPUEs of prerecruits predict the future catches of legal sizes and explored spatial patterns in the abundance of lobsters of different size and reproductive status. The standardised CPUE of prerecruits was correlated with legal size catches in only one of five areas examined. This area had a strong signal of incoming recruitment. Improving the capacity of prerecruit CPUE for predicting legal size catches several years later most likely lies with model incorporation of variables associated with catchability. The spatial distribution of catch rates showed that the area with the highest historical landings per unit area also had the highest relative abundance of prerecruits. The spatial distribution data point to further areas of research related to recruitment processes in lobster in coastal Nova Scotia.
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".