Bibliographic record
Abstract
B. Worm et al. (“Rebuilding Global Fisheries,” Research Articles, 31 July, p. [578][1]) reported cases in which effective fisheries management was based on catch restriction, gear modification, and closed areas. Consumers can also play a role in the future of fisheries. The demand for fish continues to increase yearly—is it possible to maintain the benefits of fish consumption while minimizing the risks to both human health and global fisheries? ![Figure][2] Sardines. Small pelagic fish such as sardines contain more nutrients and fewer contaminants than larger types of fish. CREDIT: [ISTOCKPHOTOS.COM][3] Harvesting from higher trophic levels in the marine food chain eventually leads us to make nutritionally and ecologically incompetent choices. We are eating the wrong kinds of fish and too many of them. There is good indication that some of the smaller fish species have more to offer to human health with less risk than larger fish closer to the top of the food chain. There are several reasons for this. Fish at the top of the food chain can become significant repositories for a range of contaminants both natural and anthropogenic and may also have low concentrations of key nutrients. The flesh of most large predator fish from warm water fisheries (big tuna, swordfish, marlin, shark) usually is low in omega-3 fatty acids and high in mercury/selenium ratios ([ 1 ][4]). Small pelagic fish, such as sardines, herrings, anchovies, and mackerel, however, have not been subject to the same overfishing pressure that has befallen almost all of the larger fish species. They not only provide higher levels of beneficial nutrients but are also significantly lower in contaminants ubiquitous to the marine food chain. They are also very affordable. Consumers' choices are more and more influenced by health and environmental considerations. That could make a difference. 1. [↵][5]1. E. Dewailly 2. et al ., Food Add. Contam. 25, 1328 (2008). [OpenUrl][6][CrossRef][7] [1]: /lookup/doi/10.1126/science.1173146 [2]: pending:yes [3]: http://ISTOCKPHOTOS.COM [4]: #ref-1 [5]: #xref-ref-1-1 View reference 1 in text [6]: {openurl}?query=rft.jtitle%253DFood%2BAdd.%2BContam.%26rft.volume%253D25%26rft.spage%253D1328%26rft_id%253Dinfo%253Adoi%252F10.1080%252F02652030802175285%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [7]: /lookup/external-ref?access_num=10.1080/02652030802175285&link_type=DOI
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.305 | 0.208 |
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".