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Record W3178374051 · doi:10.1109/cds52072.2021.00102

Predictive Modeling of Wildfires in the United States

2021· article· en· W3178374051 on OpenAlexaff
Lang Qin, Wenqian Shao, Guofei Du, Junlin Mou, Ran Bi

Bibliographic record

Venue2021 2nd International Conference on Computing and Data Science (CDS) · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDamagesLeverage (statistics)Decision treeComputer scienceDecision tree learningPrecipitationRegressionPredictive modellingMeteorologyEnvironmental scienceData miningMachine learningStatisticsGeographyMathematics

Abstract

fetched live from OpenAlex

This research utilizes wildfire records between 1911 and 2015 to train various models to predict fire size through using temperature, wind, humidity, and precipitation as features. Our results show 1) Decision Tree based Classifier outperforms both linear and ridge regression 2) Government entities can leverage our methodology to manage wildfires more efficiently, effectively, and decreasing monetary damages.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.049
GPT teacher head0.313
Teacher spread0.264 · 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 designSimulation or modeling
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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venue2021 2nd International Conference on Computing and Data Science (CDS)Same topicFire effects on ecosystemsFrench-language works237,207