AGRICULTURAL VULNERABILITY TO DROUGHT IN SOUTHERN ALBERTA: A QUANTITATIVE ASSESSMENT
Bibliographic record
Abstract
Agricultural vulnerability is generally referred to as the degree to which agricultural systems are likely to experience harm due to a stress. In this study, an existing analytical method to quantify vulnerability was adopted to assess the magnitude as well as the spatial pattern of agricultural vulnerability to varying drought conditions in Southern Alberta. Based on the farm reported data and remote sensing imagery, two empirical approaches were developed to implement vulnerability assessment in Southern Alberta at the quarter-section and 30 meter by 30 meter pixel levels. Cereal crop yield and the Standardized Precipitation Index (SPI) were specified as the agricultural wellbeing and stress pair in the study. Remote sensing data were used to generate cereal crop yield estimations, which were then implemented in vulnerability quantification. The utility of the remote sensing data source for vulnerability assessment were proved. The spatial pattern of agricultural vulnerability to different severity and duration of drought were mapped.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".