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Record W2128105622 · doi:10.1139/b02-020

The effect of light availability and basal area on cone production in <i>Abies balsamea</i> and <i>Picea glauca</i>

2002· article· en· W2128105622 on OpenAlexaffvenueabout
David F. Greene, Christian Messier, Hugo Asselin, Marie‐Josée Fortin

Bibliographic record

VenueCanadian Journal of Botany · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsConcordia UniversityUniversité LavalCenter for Northern StudiesUniversity of TorontoUniversité du Québec à Montréal
Fundersnot available
KeywordsAbies balsameaBalsamBasal areaCanopyThinningConifer coneBotanyForestryBiologyGeography

Abstract

fetched live from OpenAlex

Mean annual seed production is assumed to be proportional to basal area for canopy trees, but it is not known if subcanopy trees produce fewer seeds than expected (given their size) because of low light availability. Ovulate cone production was examined for balsam fir (Abies balsamea (L.) Mill.) and white spruce (Picea glauca (Moench) Voss) in 1998 and for balsam fir in 2000 in western Quebec using subcanopy stems, near or far from forest edges, or (at one site) planted white spruce trees in fully open conditions. A very simple light model for transmission through mature trembling aspen (Populus tremuloides Michx.) crowns and through boles near forest edges was developed to account for the effect of light receipt on cone production. The enhanced light near forest edges (e.g., recent clearcuts) leads to about a doubling of cone production for subcanopy stems. The minimum subcanopy height for cone production far from an edge is about 10 m for balsam fir and 14 m for white spruce, with these minima decreasing near edges. By contrast, the minimum height for white spruce in a plantation (full light) is about 3 m. Accounting for light receipt leads to an increase in the explained variance.Key words: balsam fir, cone production, light model, regressions, subcanopy stems, white spruce.

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)0.000
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 score0.069
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.170
Teacher spread0.165 · 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

Citations58
Published2002
Admission routes3
Has abstractyes

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