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Record W2025556311 · doi:10.1155/2009/581412

Influence of Gap Size and Position within Gaps on Light Levels

2009· article· en· W2025556311 on OpenAlexafffund
Benoît Gendreau-Berthiaume, Daniel Kneeshaw

Bibliographic record

VenueInternational Journal of Forestry Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPosition (finance)CanopyTemperate climateApparent SizeEnvironmental scienceOpticsPhysicsAtmospheric sciencesEcologyBiology

Abstract

fetched live from OpenAlex

The previous studies have reported maximum light levels at different positions within gaps but many of these studies are based on gaps of different size. The objective of this study is to evaluate the influence of gap size and position within gap on light distribution in gaps and under the canopy north of gaps in a mixedwood temperate forest. For three gap sizes, with gap widths ranging between 0.5 and 1.5 times the height of the surrounding stand, light was measured at different positions along the north-south axis in each gap using two different techniques (hemispherical photographs and instantaneous measurements). In small gaps, the position with the most light was close to the northern edge although not under the canopy north of the gap. For both methods, the position with the highest light level shifted from the north towards the center of gaps as gap size increased which clarifies some of the variability in light measurements previously observed in gap studies. Higher light levels in the northern part of small and medium gaps compared to the southern portion could allow management of a mixture of species with intolerant species in the northern portions of gaps and tolerant species to the south.

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)0.001
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 score0.257
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.364
Teacher spread0.331 · 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

Citations21
Published2009
Admission routes2
Has abstractyes

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