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Record W770110103

SEASONAL AND INTERANNUAL VARIATION IN WATER VAPOR FLUXES AND ENERGY BALANCE IN A MOIST MIXED GRASSLAND

2001· article· en· W770110103 on OpenAlexfundno aff
Linda A. Wever

Bibliographic record

VenueOpen ULeth Scholarship (OPUS) (University of Lethbridge) · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersUniversity of Lethbridge
KeywordsEvapotranspirationEddy covarianceEnvironmental scienceLatent heatCanopyGrasslandWater contentEnergy balanceSensible heatSoil waterLeaf area indexCanopy conductanceHydrology (agriculture)Atmospheric sciencesPotential evaporationPrecipitationGrowing seasonTranspirationEcosystemVapour Pressure DeficitGeographyMeteorologySoil scienceAgronomyPhotosynthesisEcologyChemistryGeology
DOInot available

Abstract

fetched live from OpenAlex

Fluxes of sensible and latent heat were measured over a grassland during 1998 and 1999 using the eddy covariance technique.The studs' objectives were to document seasonal and interannual variation in evapotranspiration and energy partitioning and to examine what factors influenced evapotranspiration.Bowen ratios were lower in 1998 (0.5-3.0) than in 1999 (2.5-8.5)due to lower evapotranspiration rates (E).Maximum E also occurred later in 1998 than in 1999: Day 188 (10.4 mmol m" 2 s" 1 ) versus Day 152 (5.6 mmol m" 2 s" 1 ).Daily evapotranspiration rates were positively correlated with net radiation, canopy conductance, plant nitrogen content, leaf area index and soil moisture.Based on calculations of the decoupling coefficient (Q).evapotranspiration was more constrained by canopy conductance in 1999 (Q<0.2) than in 1998 (Q>0.3).Evapotranspiration and energy partitioning in this grassland were sensitive to seasonal changes in soil moisture and interannual variation in spring precipitation.Annual evapotranspiration was 300 mm.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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
Published2001
Admission routes1
Has abstractyes

Explore more

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