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Record W7113484166

CENTRE FOR WATER RESEARCH: LEADING EDGE TECHNOLOGY WINS INTERNATIONAL PROJECTS

2007· article· en· W7113484166 on OpenAlexaboutno aff

Bibliographic record

VenueUWA Profiles and Research Repository (University of Western Australia) · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEnvironmental Monitoring and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsSuiteScheduleFunction (biology)Domain (mathematical analysis)Field (mathematics)Leading edgeEmerging technologiesBase (topology)
DOInot available

Abstract

fetched live from OpenAlex

The Centre for Water Research at the University of Western Australia has for decades been developing technologies for the adaptive management of natural river basins and the water bodies. The methodology consists of building an objective function that identifies a sustainable goal for the domain under consideration, a relational data base management system that is used to control the data flow and schedule from field sensors and model output, a suite of 3D hydrobiological models and a web interaction tool. These technologies are in use in projects in Singapore, Japan, Israel, Saudi Arabia, Germany, UK, Italy, the US, Canada, Brazil, Argentina, Venezuela and Colombia.

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)0.004
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.236
Threshold uncertainty score0.790

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2360.148

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 head0.134
GPT teacher head0.333
Teacher spread0.199 · 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
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

Citations0
Published2007
Admission routes1
Has abstractyes

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