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Record W3185454543 · doi:10.1002/hyp.14323

Understanding the peak growing season ecosystem water‐use efficiency at four boreal fens in the Athabasca oil sands region

2021· article· en· W3185454543 on OpenAlexafffund
Olena Volik, Richard M. Petrone, Eric Kessel, Adam Green, Jonathan S. Price

Bibliographic record

VenueHydrological Processes · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Waterloo
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaImperial Oil LimitedShell CanadaAlberta Environment and ParksSuncor Energy Incorporated
KeywordsWater-use efficiencyEnvironmental scienceEvapotranspirationEddy covariancePeatGrowing seasonBorealEcosystemTaigaWater cycleWetlandAtmospheric sciencesHydrology (agriculture)AgronomyEcologyIrrigationBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Ecosystem water‐use efficiency (WUE), a ratio between gross ecosystem production (GEP) and water loss through evapotranspiration (ET) can be helpful for the assessment of coupled peatland carbon and water cycles under anthropogenic changes in the Athabasca oil sands region (AOSR) where extensive oil sands development has been occurring since the 1960's. As such, this study assessed multiyear peak growing season variability of WUE at four fens (poor treed, poor open, treed moderate‐rich, open saline) near Fort McMurray using the eddy covariance technique combined with a set of environmental variables. Freshwater fens were characterized by WUE values within the range reported from other boreal wetlands while a saline fen had significantly lower values of WUE. Negative correlation (R s < −0.55, p < 0.05) between WUE and net radiation was observed. Moisture conditions were responsible for interannual differences in WUE, whereby increasing WUE under wetter conditions was observed. However, such a pattern was offset by decreased air temperature (T air ) resulting in moisture oversupply. This study also revealed a negative effect of wildfire on WUE due to a prominent decline in GEP and a moderate decrease in ET. WUE can be useful for monitoring the functioning of natural and constructed fens, but a better understanding of WUE variability under a wide range of climatic conditions with respect to differences in vegetation is required.

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 score0.128
Threshold uncertainty score0.439

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 studies0.0010.000
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 head0.063
GPT teacher head0.230
Teacher spread0.168 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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