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Record W2320399102 · doi:10.1061/9780784479162.245

Adverse Hydrologic and Ecologic Impacts of Wildfires in Western Watersheds

2015· article· en· W2320399102 on OpenAlexaboutno aff
Philip J. Shaller, Parmeshwar L. Shrestha, Thomas L. Deardorff, Jon R. Wren

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2015 · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceFlooding (psychology)Hydrology (agriculture)Surface runoffDebrisFlood mythWatershedAridErosionEcosystemAeolian processesFloodplainGeologyEcologyGeography

Abstract

fetched live from OpenAlex

Wildfire is a common occurrence in the semi-arid western and southwestern United States. Extended periods of drought increase the probability and severity of wildfires. The destruction of vegetation and ground litter and altered soil conditions adversely impact the hydrologic response of the watershed by increasing the runoff during post-wildfire rainfall events. The higher peak flows and greater soil erosion potential, in turn, lead to accelerated soil erosion, debris flows, and slope failures. Sediments transported to downstream waters have the potential to accentuate flooding, cause sedimentation, and change channel morphology. In addition, the sediments act as a vector for transport and redistribution of contaminants to downstream waters that may be detrimental to aquatic ecosystems. The severity of post-wildfire flooding and damage depends upon the severity of the burn, the nature of the burned vegetative cover, topography, geology, soil conditions, the timing and intensity of post-wildfire rainfall events, watershed recovery, proximity of structures to flooding sources, and engineered mitigation measures. The influence of some of the above factors is examined for the La Canada Flintridge—La Crescenta area in Southern California, which was affected by three major fire-flood sequences in the past century.

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 score0.019
Threshold uncertainty score0.670

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 science0.0000.001
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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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