Short-term climate variability and the commercial barramundi (Lates calcarifer) fishery of north-east Queensland, Australia
Bibliographic record
Abstract
The sustainable productivity of estuarine fisheries worldwide is threatened by over-fishing, habitat destruction and water impoundment. In some cases, the natural variability of freshwater inputs has been shown to affect catch when low flows reduce nutrient input and inundated nursery habitats. Historically, the annual commercial catch of barramundi (Lates calcarifer) in Queensland has been highly variable for reasons not fully understood. In conjunction with a life-cycle model, statistical analyses of climate variables and barramundi catch data from the Princess Charlotte Bay area identified several significant relationships. Warm sea surface temperatures, high rainfall, increased freshwater flow and low evaporation (all measures of an extensive and productive nursery habitat) were significantly correlated with barramundi catch 2 years later and suggest that young barramundi survival is enhanced under these conditions. Catchability was significantly increased with high freshwater flow and rainfall events in the year of catch. A forward stepwise ridge regression model that included a measure of rainfall and evaporation 2 years before catch explained 62% of the variance in catch adjusted for effort. It is recommended that the impact of climate variability be considered in the management of wild barramundi stocks and possibly other species not yet examined.
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 | 0.000 | 0.000 |
| 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".