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Record W2064539882 · doi:10.1029/2010wr009684

Effects of aspen harvesting on groundwater recharge and water table dynamics in a subhumid climate

2011· article· en· W2064539882 on OpenAlexaff
Jaime J. Carrera-Hernández, C. A. Mendoza, K. J. Devito, Richard M. Petrone, Brian Smerdon

Bibliographic record

VenueWater Resources Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsWilfrid Laurier UniversityUniversity of Alberta
Fundersnot available
KeywordsWater tableGroundwater rechargeEvapotranspirationEnvironmental scienceWaterlogging (archaeology)Hydrology (agriculture)PrecipitationSoil waterWater contentVadose zoneGroundwaterWater cycleSoil textureWater storageSoil scienceGeologyWetlandAquiferGeographyEcologyMeteorology

Abstract

fetched live from OpenAlex

Numerical experiments were developed using different water table depths and soil textures to investigate the impact of aspen harvesting on hydrological processes on the Western Boreal Plain. The effect of harvesting on soil moisture dynamics, fluxes at the water table, and water table fluctuation were compared for different harvesting scenarios simulated under wet and dry climatic cycles. Strong interaction between shallow water tables (i.e., 2 m) and atmospheric variability is observed for all soil textures and is reduced as the vadose zone thickens, particularly after a dry cycle, as a series of positive net atmospheric fluxes are needed to reduce soil moisture storage in order for recharge to occur. Because of harvesting, the water table fluxes can increase by 50 mm month −1 , while on a yearly basis, this increase can reach 200 mm yr −1 , with rainfall events taking between 1 and 5 years to become recharge (i.e., time lag). Also, the water table is expected to rise between 1 and 3.5 m, with rainfall–water table rise time lags of 1–3 years; however, the peak manifestation of harvesting on water table elevation can take up to 7 years after harvesting. The effects of aspen harvesting are more pronounced during wet cycles, and the development of forestry activities in the Boreal Plain should consider not only preceding precipitation but also the preceding precipitation–reference evapotranspiration ratio, water table depth, and soil texture. The interaction of these factors needs to be considered in order to develop sustainable forestry plans and avoid waterlogging conditions.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.001
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 head0.029
GPT teacher head0.253
Teacher spread0.224 · 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 designBench or experimental
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

Citations40
Published2011
Admission routes1
Has abstractyes

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