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Record W2533809112

Putting Climate Change into Water Resource Management: Adaptation Efforts in the U.S., U.K., Canada, Australia, and the Netherlands

2006· article· en· W2533809112 on OpenAlexaboutno aff
Hee Jun Chang, Jon Franczyk, Deg Hyo Bae

Bibliographic record

VenueJournal of Environmental Policy · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate change adaptationClimate changeAdaptation (eye)Environmental resource managementGeographyPolitical scienceEnvironmental scienceOceanography
DOInot available

Abstract

fetched live from OpenAlex

기후변화가 지역의 수자원에 영향을 미칠 것으로 예상됨에 따라 수자원 관리자들은 이에 대응한 적응전략을 수립하는 것이 필요하다. 이에 본고에서는 미국, 영국, 캐나다, 오스트렐리아, 네덜란드의 적응관리의 실태를 검토하였다. 이들 나라들은 현재의 수자원 관리관행, 제도적 장치, 기후변화의 잠재적 수자원 영향에 따라 매우 상이한 적응관리를 하고 있다. 이들 나라들의 비교연구를 통하여 기후변화에 따른 한국에서의 지속가능한 수자원 관리를 위한 정책적 관련성을 도출하였다.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.210
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; 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
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

Citations1
Published2006
Admission routes1
Has abstractyes

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