Fisheries disrupt marine nutrient cycles through biomass extraction
Bibliographic record
Abstract
Fisheries’ effects on marine life have been widely acknowledged for decades, but only recently have we considered their impact on marine nutrient cycles. Through the removal of marine biomass, fisheries represent a unique and historically novel pathway for nutrients to be extracted from the sea. Here, we examined the magnitude of carbon, nitrogen, and phosphorus extraction by industrial fisheries through large spatiotemporal scales and broad ecological contexts. Between 1960 and 2018, industrial fisheries removed approximately 431 million tonnes of carbon, 110 million tonnes of nitrogen, and 23 million tonnes of phosphorus. Nutrient extractions occurred most intensely in highly productive regions within Exclusive Economic Zones. Additionally, >53% of all nutrient extractions occurred through the removal of mid-level trophic groups and pelagic species. Our findings indicate that fisheries can remove substantial amounts of nutrients each year and warrant further studies that consider the ecosystem-level impacts of nutrient reductions. Fisheries remove substantial amounts of carbon, nitrogen, and phosphorus from the ocean each year through marine biomass, according to analysis of industrial catch and nutrient composition data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".