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Record W7097590079

Clearcutting Impacts on Snow Accumulation and Melt in a Northern Hardwood Forest

2014· article· en· W7097590079 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClearcuttingSnowSnowmeltSurface runoffMeltwaterWatershedHydrology (agriculture)CanopyHardwood
DOInot available

Abstract

fetched live from OpenAlex

Snow accumulation and melt on north- and south-facing slopes in the Turkey Lakes Watershed (TLW) in central Ontario were compared for a mature hardwood maple stand and an adjacent clearcut. Snow accumulation in the clearcut exceeded that in the forest. Melt was significantly larger in the south-facing clearcut and forest relative to corresponding north-facing sites. Daily melt in the clearcut on the north-facing slope was slightly greater and more spatially variable than in the adjacent forest. Nevertheless, the control of aspect on the spatial variations in melt was larger than that due to clearcutting. Micro-scale variations in canopy density did not explain inter-point differences in daily melt within either the clearcut or the forest. The hydrological consequences of greater pre-melt snow water equivalent and larger daily melt in clearcuts include quicker delivery of meltwater to the soil surface and promotion of rapid near-surface runoff to receiving waters relative to undisturbed forest stands at TLW.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.547
Threshold uncertainty score0.911

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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