Reconstruction of marine fisheries catches for New Zealand (1950-2010)
Bibliographic record
Abstract
New Zealand’s reported marine fisheries catch statistics are incomplete due to the omission of significant amounts of ‘invisible’ (i.e. unreported) landings in industrial fisheries, of fish that are discarded at sea, and of fish taken by recreational and customary fishers. This reconstruction accounts for unreported catch to provide a more comprehensive picture of total marine fisheries catches taken from New Zealand’s waters from 1950 to 2010. We use publically available official catch data from the Ministry for Primary Industries to reconstruct a baseline. We augment these baseline data using stock assessment reports, peer-reviewed literature, grey literature, data obtained under the Official Information Act, and data from a wide range of industry experts and personnel. New Zealand’s reconstructed catch totalled 38.1 million tonnes (t) over the 61 year period. This indicates the actual catch was about 2.7 times the 14 million t reported to the FAO on behalf of New Zealand for the same time period. New Zealand introduced a Quota Management System (QMS) in 1986, to ensure fisheries resource sustainability and improve reporting. The total catch since then is conservatively estimated to be 2.1 times greater than that reported to the FAO. Unreported industrial catch and discards account for the vast majority of the discrepancy. Recreational and customary catch was 0.51 million t for the same period. From 1960 until 2010, 43% of all commercial catch was caught by foreign flagged vessels, which dominated the catching of hoki (Macruronus novaezelandiae), squid (Nototodarus sloanii), jack mackerels (Trachurus spp.), barracouta (Thyrsites atun), and southern blue whiting (Micromesistius australis). These five species comprised 53% of reported landings from 1950-2010. These were also some of the most misreported and discarded species over the time period considered. Some estimates of unreported catches and discards are included in governmental stock assessment reports, but the lack of comprehensive and transparent reporting threatens the integrity of the QMS. Improving the transparency and reliability of fisheries data reporting is essential for fisheries management and sustainability. The future sustainability and certification of fisheries will depend on how the government addresses the under-reporting problems, which have long been a cause of concern.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".