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Record W2514368667 · doi:10.1016/j.ecolind.2016.07.051

Understanding the LCA and ISO water footprint: A response to Hoekstra (2016) “A critique on the water-scarcity weighted water footprint in LCA”

2016· article· en· W2514368667 on OpenAlexaff
Stephan Pfister, Anne‐Marie Boulay, Markus Berger, Michalis Hadjikakou, Masaharu Motoshita, Tim Hess, Bradley G. Ridoutt, Jan Weinzettel, Laura Scherer, Petra Döll, Alessandro Manzardo, Montserrat Núñez, Francesca Verones, Sébastien Humbert, Kurt Buxmann, Kevin G. Harding, Lorenzo Benini, Taikan Oki, Matthias Finkbeiner, Andrew D. Henderson

Bibliographic record

VenueEcological Indicators · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversité de Sherbrooke
FundersU.S. Environmental Protection Agency
KeywordsWater useWater scarcityLife-cycle assessmentFootprintEnvironmental scienceResource (disambiguation)Environmental economicsEnvironmental resource managementNatural resource economicsWater resourcesWater resource managementBusinessComputer scienceEconomicsProduction (economics)Ecology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.043
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.060
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.113
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.005
Science and technology studies0.0080.045
Scholarly communication0.0180.033
Open science0.0070.011
Research integrity0.0460.077
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.027
GPT teacher head0.246
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

Quick stats

Citations218
Published2016
Admission routes1
Has abstractno

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