MétaCan
Menu
Back to cohort
Record W2054346797 · doi:10.1139/x02-167

Effects of a wide gradient of retained tree structure on understory light in coastal Douglas-fir forests

2003· article· en· W2054346797 on OpenAlexvenueno aff
C. Ronnie Drever, Kenneth P. Lertzman

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsnot available
Fundersnot available
KeywordsUnderstoryMicrositeCanopyEnvironmental scienceForestryTree canopyRange (aeronautics)Diameter at breast heightEcologyAtmospheric sciencesGeographyBiologyBotanyPhysicsMaterials science

Abstract

fetched live from OpenAlex

We characterize understory light of seven stands that varied along a gradient of tree retention. Using hemispherical canopy photographs and digital image, we estimated gap light or solar radiation reaching the understory through the canopy. Using nonlinear regressions, we related gap light to several structural attributes in the examined silvicultural treatments. The silvicultural treatments affected both the median and range of gap light in the understory. As overstory removal increased from uncut second growth to green-tree retention, the median value of light increased from 8 to 68% full sun, while the range of light increased from 3–22% to 26–88% full sun. We found strong, significant, and negative nonlinear relationships between gap light at a particular microsite (0.04 ha) in the understory and the height, diameter at breast height, density, and volume of surrounding retained trees (r a 2 = 0.77–0.94). These relationships can aid planning of treatments that retain forest structure, such as variable retention, by allowing predictions of understory light from commonly used field data. These predictions allow forest managers to understand some of the ecological consequences and tradeoffs associated with retaining structure during harvesting.

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 score0.602
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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

Citations71
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicRemote Sensing and LiDAR ApplicationsFrench-language works237,207