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Record W1793933723 · doi:10.1002/eco.1346

Comparative studies on turbulent fluxes measured over burned and unburned sites of a sagebrush‐dominated mountain

2012· article· en· W1793933723 on OpenAlexaff
Ayodeji B. Arogundade, Wenguang Zhao, Russell J. Qualls

Bibliographic record

VenueEcohydrology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsKimberly-Clark (Canada)
FundersNational Science Foundation
KeywordsSensible heatLatent heatEnvironmental scienceEddy covarianceBowen ratioAtmospheric sciencesEvapotranspirationHydrology (agriculture)DaytimeClimatologyMeteorologyEcosystemGeologyEcologyGeography

Abstract

fetched live from OpenAlex

ABSTRACT Fire is a major disturbance that causes rangeland change. In this study, we compared the turbulent fluxes of carbon dioxide and sensible and latent heat, measured over burned and unburned sites of a sagebrush‐dominated mountain in southern Idaho during the late summer of 2006. The outcome of the investigation shows that fire altered the horizontal components of turbulence intensity ( i u and i v ) as well as the partitioning of radiant energy between latent and sensible heat fluxes. Average daytime Bowen ratios ( β ) at the burned and unburned sites were 2.03 and 1.87. On the basis of Bowen ratios determined from eddy covariance measurements of sensible and latent heat fluxes, the sensible heat fluxes were relatively more significant at the burned site than at the unburned site most of the time, and the converse was true of the latent heat fluxes. The exception to this was for the few days following heavy rainfall, when near‐surface soil moisture increased the evapotranspiration at the burned site more than at the unburned site until the shallow moisture supply was depleted. By means of ratios of CO 2 /( H + LE), fluxes, carbon sequestration was found to be more significant at the unburned site, declining at both sites as summer progressed but declining more rapidly at the burned site than at the unburned site. Copyright © 2012 John Wiley & Sons, Ltd.

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 score0.315
Threshold uncertainty score0.428

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 studies0.0000.000
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 head0.025
GPT teacher head0.269
Teacher spread0.243 · 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
Published2012
Admission routes1
Has abstractyes

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