Bridging the Data Gap in the Water Scarcity Footprint by Using Crop-Specific AWARE Factors
Bibliographic record
Abstract
The assessment of the water scarcity footprint of products emerged as an important step in supporting water management strategies. Among others, the AWARE methodology was published as a consensus-based indicator to perform such an assessment at a watershed level and monthly scale. The need to adopt such a detailed resolution, however, collides with the availability of data, so that general year and country-wide factors are commonly used. The objective of this study is to develop and verify the applicability of 26 crop-specific water scarcity characterization factors to help assess the water scarcity footprint when data and information availability is limited. To do so, a weighted average consumption approach was adopted, starting from local AWARE characterization factors and local crop-specific water consumption. The resulting factors, ranging from 0.19 m3/ton eq for “other perennial crop” in Brunei to 9997 m3/ton eq for “other annual crop” in Mauritania, illustrate the large variability of potential water scarcity impacts. Factors were applied to the water consumption of selected crops to assess their water scarcity footprint. The results of the study confirmed that the use of crop-specific factors is recommended as they are a better proxy of water scarcity in a region when compared to their national generic counterparts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".