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Record W7033342879

Pollution from urban land use in the Grand and Saugeen Watersheds

2018· report· en· W7033342879 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2018
Typereport
Languageen
FieldEnvironmental Science
TopicHydrological Forecasting Using AI
Canadian institutionsnot available
Fundersnot available
KeywordsPollutantPollutionUrban areaLand useHydrology (agriculture)Air pollutionEstimationRiver pollution
DOInot available

Abstract

fetched live from OpenAlex

Under the IJC-PLUARG program, seven urban study areas in the Grand River and Saugeen River pilot watersheds were studied between 1974 and 1977 to examine a wide variety of potential sources of pollution to the boundary waters of the Great Lakes., namely: Kitchener - Waterloo - Cambridge, Schneider Creek, Montgomery Creek, Guelph, New Hamburg, Durham and Allan Park. This technical report is concerned with the following purposes: 1) the determination of the pollutants derived from urban areas, 2) the estimation of the magnitude of the pollutant contributions from urban areas in terms of unit-area loadings, 3) the determination of the sources of pollutants within urban areas and the evaluation of their relative significance, 4) the investigation of the nature of pollutant transport from urban areas.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.220
Teacher spread0.184 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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