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Record W2081232524 · doi:10.5194/piahs-366-23-2015

Water Security – science and management challenges

2015· article· en· W2081232524 on OpenAlexaff
H. S. Wheater

Bibliographic record

VenueProceedings of the International Association of Hydrological Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsDisciplineInterdependenceHuman systems engineeringEnvironmental resource managementWater securityEnvironmental planningEcosystem managementEarth system scienceWater resourcesScale (ratio)Natural (archaeology)Land managementLand useEnvironmental scienceSociologyGeographyEngineeringEcologySocial scienceCivil engineeringEcosystem

Abstract

fetched live from OpenAlex

Abstract. This paper briefly reviews the contemporary issues of Water Security, noting that current and prospective pressures represent major challenges for society. It is argued that, given the complex interdependencies and multi-faceted nature of these challenges, new trans-disciplinary science is needed to support the development of science-based policy and management. The effects of human society on land and water are now large and extensive. Hence we conclude that: (a) the management of water involves the management of a complex human-natural system, and (b) potential impacts of the human footprint on land and water systems can influence not only water quantity and quality, but also local and regional climate. We note, however, that research to quantify impacts of human activities is, in many respects, in its infancy. The development of the science base requires a trans-disciplinary place-based focus that must include the natural sciences, social sciences and engineering, and address management challenges at scales that range from local to large river basin scale, and may include trans-boundary issues. Large basin scale studies can provide the focus to address these science and management challenges, including the feedbacks associated with man’s impact from land and water management on regional climate systems.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2015
Admission routes1
Has abstractyes

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