Fishers’ response to temperature change reveals the importance of integrating human behavior in climate change analysis
Bibliographic record
Abstract
Climate change will reshape ecological dynamics. Yet, how temperature increases alter the behavior and resource use of people reliant on natural resources remains underexplored. Consequent behavior shifts have the potential to mitigate or accelerate climate impacts on livelihoods and food security. Particularly within the small-scale inland fisheries that support approximately 10% of the global population, temperature changes likely affect both fish and fishers. To analyze how changing temperatures alter households' fishing behavior, we examined fishing effort and fish catch in a major inland fishery. We used longitudinal observational data from households in Cambodia, which has the highest per-capita consumption of inland fish in the world. Higher temperatures caused households to reduce their participation in fishing but had limited net effects on fish catch. Incorporating human behavioral responses to changing environmental conditions will be fundamental to determining how climate change affects rural livelihoods, food production, and food access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".