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Record W1992110634 · doi:10.1080/17445647.2014.934747

Mapping a pollution index for the transboundary Red River Valley, Asia, 2009–2011

2014· article· en· W1992110634 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Maps · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPollutionWatershedRiver pollutionWater resource managementEnvironmental scienceGovernment (linguistics)Scale (ratio)Index (typography)GeographyHydrology (agriculture)Environmental planningEnvironmental resource managementEnvironmental protectionCartographyGeologyComputer science

Abstract

fetched live from OpenAlex

A transboundary river is a river that flows through two or more countries. Due to the international policies of the government in each country, it is difficult to investigate the pollution conditions in a transboundary watershed. In this study, pollution sources in the Red River Valley, Southeast Asia were derived from Google Earth by visual interpretation. Based on the derived pollution sources and a simple scoring system, a pollution index map was produced, which provides useful information for intergovernmental cooperation on the water environment in this transboundary watershed. In addition, the method mentioned in this article suggests an efficient and low-cost way to investigate pollution conditions at large scale, which can also be applied to other transboundary watersheds in the future.

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.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.257
Teacher spread0.235 · 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