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Record W2508754706 · doi:10.1002/eco.1780

Short‐term selective thinning effects on hydraulic functionality of a temperate pine forest in eastern Canada

2016· article· en· W2508754706 on OpenAlexaffabout
Rachel A. Skubel, Myroslava Khomik, Jason Brodeur, Robin Thorne, M. Altaf Arain

Bibliographic record

VenueEcohydrology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsThinningEnvironmental scienceEvapotranspirationEddy covarianceTemperate forestEcosystemForest ecologyForest managementTemperate rainforestAgroforestryHydrology (agriculture)ForestryEcologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Forest plantations are commonly used to restore the ecological and hydrological functionality of landscapes. In this study, we investigated the hydrological response of a 74‐year‐old pine plantation forest in southern Ontario, Canada to a selective thinning, wherein 30% of trees were harvested in winter of 2012. Tree‐level and ecosystem‐level water fluxes were monitored from 2011 to 2013 using sapflow and eddy‐covariance techniques, and were compared with an adjacent 39‐year‐old pine plantation forest that was not thinned. In the 74‐year‐old forest, transpiration declined after thinning in the 2012 growing season, while tree‐level water transport increased despite a severe drought. Time lag between sapflow and ecosystem evapotranspiration showed that the older stand had a significantly shorter time lag when compared to the younger stand, particularly in the thinning year. Linear regression modeling indicated that this was likely due to higher soil moisture status in the older stand versus the younger forest. Hydraulic redistribution of soil water was apparent during drought periods in both forests and did not appear to be negatively affected by the thinning treatment in the older forest. We conclude that selective low density thinning did not negatively impact the forest's response to seasonal drought, and that the availability of more soil moisture as a result of thinning may improve the resilience of the forest to future climatic extreme events such as drought.

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 score0.472
Threshold uncertainty score0.948

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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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