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Record W4388015081 · doi:10.18280/ijsdp.181033

Environmental Risk Assessment of Spring Floods in the Akmola Region of Kazakhstan

2023· article· en· W4388015081 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsSpring (device)GeographyEnvironmental scienceEnvironmental planningEnvironmental protectionEnvironmental resource managementPhysical geographyEngineering

Abstract

fetched live from OpenAlex

The relevance of the subject matter in this study is conditioned by a high degree of risk of spring floods in Akmola region, significant damage to the economy of the region and the need to develop and implement effective measures to counteract this natural situation.The purpose of the study is to develop a proper assessment of the environmental risk of spring floods and their consequences in the flooded areas of Akmola region.The methodological approach in this study is based on a combination of system analysis methods.They analyse the degrOctober 2023ee of probability of various kinds of environmental risks caused by spring floods and investigate the regularities of development of negative consequences of floods as a whole and in Akmola Region of the Republic of Kazakhstan in particular.In the course of this study, findings have been made indicating the serious consequences of spring floods for economic activity in the areas affected by seasonal flooding of water bodies.The findings also indicate the need to develop and implement specific measures to prevent spring floods caused by serious disturbances to economic activity in water protection zones.The findings of this study and the scientific conclusions drawn from them are of significant practical significance for agricultural workers in Akmola region, who have to cope with the consequences of spring floods in the area where they operate.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.226
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