Building consensus on a generic water scarcity indicator for LCA-based water footprint: preliminary results from WULCA
Bibliographic record
Abstract
Consuming water can affect human health (e.g. by reducing availability of irrigation water and hence food availability), ecosystems (by decreasing water availability for terrestrial/aquatic species) and future generations (by depleting non-renewable resources). However, no standard method exists to quantify the stress on water without favoring any of these areas of protection. Stress/scarcity indexes have fo-cused on an anthropocentric perspective, and a few on an ecocentric perspective. We explore the possibility of developing an indicator considering the water resource as a whole and propose a method which is not centered on an area of protection but rather assesses the ex-tent to which all water demand and availability differ within a watershed (i.e. hydrocentric). This concept can eventually serve as a single metric to assess potential impacts from water use and be used consistently in the application of the upcoming ISO standard and for ecolabelling of food and energy products.
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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 | 0.115 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".