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Record W2122625491 · doi:10.3390/su7043900

Dual-Level Material and Psychological Assessment of Urban Water Security in a Water-Stressed Coastal City

2015· article· en· W2122625491 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

VenueSustainability · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicKnowledge Management and Technology
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsDual (grammatical number)Water securityEnvironmental planningWater resource managementEnvironmental resource managementEnvironmental scienceWater resources

Abstract

fetched live from OpenAlex

The acceleration of urbanization and industrialization has been gradually aggravating water security issues, such as water shortages, water pollution, and flooding or drought disasters and so on. Water security issues have become a great challenge to urban sustainable development. In this context, we proposed a dual-level material and psychological assessment method to assess urban water security. Psychological security coefficients were introduced in this method to combine material security and residents’ security feelings. A typical water-stressed coastal city in China (Dalian) was chosen as a case study. The water security status of Dalian from 2010 to 2012 was analysed dynamically. The results indicated that the Dalian water security statuses from 2010 to 2012 were basically secure, but solutions to improve water security status and solve water resource problems are still required. This dual-level material and psychological assessment for urban water security has improved conventional material assessment through the introduction of psychological security coefficients, which can benefit decision-making for urban water planning, management and protection.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
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)0.0000.000

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 head0.114
GPT teacher head0.411
Teacher spread0.297 · 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