Understanding human adaptation to drought: agent-based agricultural water demand modeling in the Bow River Basin, Canada
Bibliographic record
Abstract
The farmers in the Bow River Basin (BRB), Canada, have adopted water conservation strategies to reduce water needs. This reduction, however, encouraged the expansion of irrigation, which may rebound agricultural water demands. This paradox requires an understanding of human adaptation to drought by mapping individual farmers’ water conservation decisions to the dynamics of the basin-wide water demand. We develop an agent-based agricultural water demand (ABAD) model, simulating farmers’ behavior in adopting new on-farm irrigation systems and/or changing crop patterns in response to drought conditions in the BRB. ABAD demonstrates (1) how farmers’ attitude toward profits, risk aversion, environmental protection, social interaction, and irrigation expansion explains the dynamics of the water demand and (2) how the conservation program may paradoxically lead to the rebound phenomenon. ABAD, subject to its conceptualization limitations, can be used for exploration and scenario analysis of future agricultural water demand in response to water conservation programs in the BRB.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".