Bibliographic record
Abstract
The human appetite for seafood has intensified and so has overfishing and damage to marine ecosystems. However, the true demand for seafood is often not captured in the national or United Nations Food and Agriculture Organization (FAO) statistics. The underreporting of catches is prevalent worldwide, which inevitably leads to mismanagement, and justifies data improvements via catch reconstructions. For instance, marine fisheries catch reconstructions for 1950 to present for Mozambique and the United Republic of Tanzania show that the small-scale fisheries sectors in both countries are underreported. Overall, reconstructed marine fisheries catches for Mozambique and Tanzania were respectively 6.2 and 1.7 times greater than those reported by FAO. Similarly, shark catches have been underreported globally, and reconstruction of Ecuador’s mainland shark landings for 1979 to 2004 shows that shark landings were an estimated 7,000 tonnes per year, or nearly half a million sharks, and 3.6 times greater than those reported by FAO from 1991 to 2004. Over the past decades, as we realize fish catches are larger than officially reported and demand for seafood is outstripping the availability of wild resources, conservation groups have been attempting to change patterns of household consumption, particularly in North America and Europe. These groups aim to reduce overfishing and encourage sustainable fishing practices using tools like consumer awareness campaigns and seafood certification schemes. But many factors impede these efforts, such as the renaming and mislabeling of seafood, the absence of a significant price premium for certified seafood, and, most importantly, a lack of demonstrably improved conservation status for the species that are meant to be protected. This dissertation presents market-based initiatives that may strengthen current initiatives, e.g. global adoption of chain of custody standards, working higher in the demand chain, connecting seafood to climate change, and diverting small fish away from the fishmeal industry into human food markets. Also, conservation groups should consider investing as much effort into the elimination of harmful fisheries subsidies, the primary perverse incentive encouraging excess fishing capacity, as they put into altering consumer behavior. Finally, conservationists can learn from the latest research on the psychology of savings and investment. If these market efforts complement more marine protected areas and regulations, it may be possible to ensure fish as food and wildlife for both current and future generations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 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".