Net negative nutrient yields in a bait-consuming fishery
Bibliographic record
Abstract
Abstract Efforts to achieve sustainable food systems are impeded by inefficiencies associated with the use of agricultural land and resources to grow feed for animals, rather than food for direct consumption by people. In contrast, the unspoken assumption about fisheries, which are a key source of protein and micronutrients, is that they are inherently net-positive producers of food, as they appear to require no intentional inputs of resources that could otherwise be directly consumed by people. However, this assumption may not hold true for all fisheries. One such fishery is the Maine fishery for American lobster (Homarus americanus), which for decades has used substantial amounts of Atlantic herring (Clupea harengus) as bait in its traps. Here, we evaluate the Maine lobster fishery’s production of a suite of nutrients both before and after consideration of its use of Atlantic herring as bait. Despite several sources of uncertainty, our results indicate that the Maine lobster fishery has likely been a net consumer of multiple nutrients in recent years. This stems from both the scale of herring bait use in the lobster fishery, and from herring’s comparatively high edible biomass yield and nutrient content. To our knowledge, this is the first example of a fishery consuming more nutrients, through bait, than it produces through landings. Identifying and addressing such inefficiencies will ensure that fisheries contribute to sustainable food production.
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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 | 0.000 | 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) | 0.002 | 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".