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Record W131128673

Climatology and Synoptic Evolution of Major Forest Fire Events Over the Northeast U.S.

2012· dissertation· en· W131128673 on OpenAlexaboutno aff
Joseph B. Pollina

Bibliographic record

VenueSUNY Digital Repository Support (State University of New York System) · 2012
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersU.S. Forest ServiceStony Brook UniversityU.S. Department of Agriculture
KeywordsClimatologyGeographySynoptic scale meteorologyMeteorologyEnvironmental scienceGeology
DOInot available

Abstract

fetched live from OpenAlex

This study presents a spatial and temporal climatology of major wildfires (>100 acres burned) in the Northeast U.S from 1999 to 2009 and the meteorological conditions associated with these events. About 59% of the wildfire events in this region occur in April and May, with &sim76% of all wildfires over the higher elevation (> 1000 m) regions of the Northeast occurring in these months, while &sim53% occur in the Appalachian lee and the coastal plain. The other 41% of wildfires occur in the summer, fall and early spring months. The synoptic flow patterns associated with Northeast wildfires were classified using the North American Regional Reanalysis (NARR). The most common synoptic pattern in the Appalachian high terrain region is a surface high pressure centered over the northern Appalachians (&sim46% of events). For the coastal plain fire events, the most common pattern (&sim46%) is an anticyclone extending southward from southeastern Canada and Great Lakes to the Northeast. Trajectories show that the pre-high pattern shows the greatest subsidence, greatest decrease in relative humidity, and greatest increase in temperature. Terrain sensitivity studies show that there is a 1&degC to 2&degC increase in temperature and a 0%-8% decrease in RH when there is a southwesterly flow downsloping event over the NEUS.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueSUNY Digital Repository Support (State University of New York System)Same topicFire effects on ecosystemsFrench-language works237,207