Reconstructing Ireland’s marine fisheries catches: 1950-2010
Bibliographic record
Abstract
The wasteful practice of discarding catch is one of the major problems associated with European fisheries. Despite this, estimates of discarded catch are not included in the ‘Official Catch Statistics’ database (1905 to present) collected and maintained by the International Council for the Exploration of the Sea (ICES). Furthermore, removals through recreational sea angling and estimates of other forms of unreported landings are often also missing from this dataset. Here, total discarded catch and unreported landings made by Irish commercial fishing vessels, and the total amount of fish caught and retained through Irish sea angling activities within the Northeast Atlantic from 1950 to 2010 have been estimated. Total reconstructed catches were 19.3% and 20.9% higher than the officially recorded total landings as reported by ICES from the Northeast Atlantic, and those estimated as being from within the Irish Exclusive Economic Zone (EEZ), respectively. Discarded catch was proportionately the largest component of the reconstruction, representing 12.7% of the total catch within the Irish EEZ. The Irish catch reconstruction presented here is by no means assumed to represent the complete record of total removals and the authors encourage further efforts to improve upon this attempt. However, considering the current absence of estimated values for discarded catch, recreational removals and other unreported landings from officially and publicly reported data, we feel that our reconstruction provides an improved baseline estimate of more accurate total Irish marine fisheries catch that has not previously been made publicly available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".