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Record W2902958126 · doi:10.1016/s2542-5196(18)30228-6

Watersheds in planetary health research and action

2018· article· en· W2902958126 on OpenAlexaff
Aaron Jenkins, Anthony Capon, Joel Negin, Ben J. Marais, Tania C. Sorrell, Margot W. Parkes, Pierre Horwitz

Bibliographic record

VenueThe Lancet Planetary Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsWatershedEcosystem healthSustainabilityGeographyDrainage basinScopusEcosystemEcologyEnvironmental resource managementEcosystem servicesEnvironmental scienceMEDLINECartographyPolitical scienceBiology

Abstract

fetched live from OpenAlex

Watersheds (also known as water catchments and river basins) are recognised in contemporary science as important natural systems in which to investigate the complex socioecological foundations of health.1 A watershed is the spatially bound geophysical unit within which surface and shallow groundwater drain to a single collecting stream or river (see appendix). Watersheds are physical and abstract systems: they are open and hydrologically permeable, yet can be represented as functionally distinct.

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 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)0.017
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.009
Science and technology studies0.0070.030
Scholarly communication0.0200.019
Open science0.0030.020
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0650.008

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 head0.443
GPT teacher head0.452
Teacher spread0.009 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations56
Published2018
Admission routes1
Has abstractyes

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