Data from: Assessing the ecosystem-level consequences of a small-scale artisanal kelp fishery within the context of climate-change
Bibliographic record
Abstract
Coastal communities worldwide rely on small-scale artisanal fisheries as a means of increasing food security and alleviating poverty. Even small-scale fishing activities, however, are prone to resource depletion and environmental degradation, which can erode livelihoods in the long run. Thus, there is a pressing need to identify viable and resilient artisanal fisheries, and generate knowledge to support management within the context of a rapidly changing climate. We examined the ecosystem-level consequences of an artisanal kelp fishery (Macrocystis pyrifera), finding minimal impacts of small-scale harvest on kelp recovery rates, survival, and biomass dynamics, and abundances of associated commercial and culturally important fish species. These results suggest that small-scale harvest poses minimal trade-offs for the other economic benefits provided by these ecosystems, and their inherent, spiritual, and cultural value to humans. However, we detected a negative impact of warmer seawater temperatures on kelp recovery rates following harvest, indicating that the viability of harvest, even at small scales, may be threatened by future increases in global ocean temperature. This suggests that negative impacts of artisanal fisheries may be more likely to arise in the context of a warming climate, further highlighting the widespread effects of global climate change on coastal fisheries and livelihoods.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.022 |
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".