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Methods for Calculation of Water Environment Capacity of Small and Medium River Channels

2012· article· en· W2056470338 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

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Changes in China
Canadian institutionsCanadian Hydrographic Service
FundersDivision of Materials ResearchMinistry of Water Resources
KeywordsGeneralizationPollutantWater qualityFunction (biology)PollutionMathematical optimizationComputer scienceIdeal (ethics)Environmental scienceEnvironmental engineeringMathematicsMathematical analysisChemistry

Abstract

fetched live from OpenAlex

Methods for calculation of water environment capacity can be divided into two types in general, one type is ideal water environment capacity method and the other is pollution source generalization method. This paper proposes another way of generalization, uniform generalization method, assuming that distribution of the amount of pollutants discharged is uniform in the lengthways direction. In consideration of the practical demand for administration of water resources protection, a formula for calculation of water environment capacity for different combinations of environment function regions is presented,based on the water quality targets of function regions, with the numerical or analytical methods of 1-D water quality model. The proposed method provides a simple and effective method for water resources management and planning.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.108
GPT teacher head0.395
Teacher spread0.287 · 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