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Record W4385060835 · doi:10.53328/fclg9188

Global Water Security 2023 Assessment

2023· report· en· W4385060835 on OpenAlexfundno aff
Charlotte MacAlister, Guillaume Baggio, Duminda Perera, Manzoor Qadir, Lina Taing, Vladimir Smakhtin

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersGlobal Affairs CanadaGovernment of CanadaMcMaster UniversityUNICEF
KeywordsScalabilityWater securityMember statesPolitical scienceBusinessComputer scienceWater resourcesInternational tradeEcologyBiologyEuropean union

Abstract

fetched live from OpenAlex

UNU INWEH's mission is to help resolve pressing water challenges that are of concern to the United Nations, its Member States, and their people, through critical analysis and synthesis of existing bodies of scientific discovery; targeted research that identifies emerging policy issues; application of on-the-ground scalable science-based solutions to water issues; and global outreach.UNU INWEH carries out its work in cooperation with the network of other research institutions, international organisations and individual scholars throughout the world.UNU INWEH is an integral part of the United Nations University (UNU) -an academic arm of the UN, which includes 13 institutes and programmes located in 12 countries around the world, and dealing with various issues of development.UNU INWEH was established, as a public service agency and a subsidiary body of the UNU, in 1996.Its operations are secured through long-term host-country and core-funding agreements with the Government of Canada.The Institute is located in Hamilton, Canada, and its facilities are supported by McMaster University.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0300.015

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 head0.036
GPT teacher head0.351
Teacher spread0.315 · 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 designObservational
Domainnot available
GenreOther

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

Citations39
Published2023
Admission routes1
Has abstractyes

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