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Record W2750494147 · doi:10.22438/jeb/38/5/mrn-331

Application of Soil Water Assessment Tool (SWAT) to suppress wildfire at Bayam Forest, Turkey

2017· article· en· W2750494147 on OpenAlexaff
Mustafa Tüfekçioğlu, Mehmet Yavuz, George Ν. Zaimes, Musa Dinc, Paschalis Koutalakis, Aydın Tüfekçioğlu

Bibliographic record

VenueJournal of Environmental Biology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsCanadian Journal of Communication (Canada)
FundersInterreg
KeywordsEnvironmental scienceSoil and Water Assessment ToolSWAT modelHydrology (agriculture)WatershedSoil waterSTREAMSWater resource managementWater resourcesSustainabilityDigital elevation modelStreamflowGeographySoil scienceRemote sensingEcologyDrainage basinComputer scienceGeologyCartography

Abstract

fetched live from OpenAlex

Readily available water resources are a key for wildfire suppression. Hydrologic models are a practical and essential tool for understanding the processes of hydrology and managing water resources, but have not been utilized as frequently for wildfire suppression. The goal of the present study was to use the Soil Water Assessment Tools (SWAT) model to determine whether the stream water could be managed sustainably in wildfire suppression at the Bayam Forest District in Kastamonu Province, Turkey.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.644

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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