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Record W4362559942 · doi:10.3390/w15071383

Compilation of Water Resource Balance Sheets under Unified Accounting of Water Quantity and Quality, a Case Study of Hubei Province

2023· article· en· W4362559942 on OpenAlexaff
Liang Yuan, Liwen Ding, Weijun He, Yang Kong, Thomas Stephen Ramsey, Dagmawi Mulugeta Degefu, Xia Wu

Bibliographic record

VenueWater · 2023
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsResource (disambiguation)Water resourcesWater qualityWater balanceBusinessQuality (philosophy)Environmental scienceBalance sheetWater resource managementEnvironmental economicsBalance (ability)ChinaAccountingNatural resource economicsEnvironmental resource managementEconomicsComputer scienceGeographyEngineering

Abstract

fetched live from OpenAlex

This article discusses the issues caused by traditional water resource development and utilization, as well as policy issues in China that have led to a water crisis. The article proposes a theoretical approach along with a quantitative accounting of water resources, in order to solve these problems. To improve the value accounting method for water resources, the study focuses on a unified accounting perspective of water quantity and quality, allowing for an evaluation of water use efficiency and quality. The study uses prefecture-level cities in Hubei Province as a case study and finds that the water use efficiency of these cities has constantly improved, while water quality has shown an annual improvement. Water resource assets, liabilities, and net assets have increased, but with fluctuations. The study shows differences in water resource assets, liabilities, and net assets in the eastern, central, and western regions of Hubei Province. The unified accounting perspective of water quantity and quality provides a new idea and method for the preparation of water resource balance sheets and will effectively improve the management level and efficiency of water resources.

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 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.252
Teacher spread0.218 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations23
Published2023
Admission routes1
Has abstractyes

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