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Record W4393821786 · doi:10.5281/zenodo.6207238

AWASH Water Stress Metrics

2020· dataset· en· W4393821786 on OpenAlexaff
James Rising, Laureline Josset, Tara J. Troy, Upmanu Lall

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWater stressStress (linguistics)BiologyHorticulturePhilosophyLinguistics

Abstract

fetched live from OpenAlex

This data repository contains county-level water stress results from the analysis performed in "The importance of infrastructure and national demand to represent constraints on water supply in the United States". The largemaps.pdf file contains the figures from the paper in a larger format. summary.csv shows for each county the median water stress (across years) as well as the range of water stress, for each of the four scenarios (local runoff, river network only, with canals, and with canals and reservoirs). results.zip provides the raw results. The zipped file contains a folder 'results', which includes the spatiotemporal optimization outcomes either for surface water demands or total demands (`-alldemand` files). The `byfips-...` files then show metrics for the excess stress from all demands across counties, and the `fipstime-...` files show these excess stress results across counties and months.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0030.009
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0290.155

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 head0.054
GPT teacher head0.255
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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