MétaCan
Menu
Back to cohort
Record W4238173652 · doi:10.1139/x99-223

Seed abundance versus substrate limitation of seedling recruitment in northern temperate forests of British Columbia

2000· article· en· W4238173652 on OpenAlexvenueaboutno aff
Philip T. LePage, Charles D. Canham, K Dave Coates, Paula Bartemucci

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSeedlingCanopyAbundance (ecology)Tree canopyBiologySubstrate (aquarium)Temperate rainforestTemperate forestEcologyAgronomyTemperate climateEcosystem

Abstract

fetched live from OpenAlex

We examine the influence of (i) the spatial distribution and abundance of parent trees (as seed sources) and (ii) the abundance and favourability of seedbed substrates, on seedling recruitment for the major tree species in northwestern interior cedar-hemlock forests of British Columbia, under four levels of canopy openness (full canopy, partial canopy, large gap, and clearcut). Substrate distribution varied with canopy openness, and substrate favourability was a function of both canopy openness and seedling species. Lack of suitable substrates was the predominant factor limiting seedling density under full canopies. Partial canopy and gap sites provided a broad range of favourable substrates in close proximity to parent trees, resulting in the highest observed seedling densities. There was much higher effective dispersion of seedlings away from parent trees in gaps than in the partially cut stands. Seedling dispersion to clearcut sites was poor with seedlings being tightly restricted to a narrow band along the forest edge. Thus, seedling recruitment in these forests was a reflection of the interaction between the abundance of seed and substrate favourability, and the relative importance of these factors varied significantly with canopy structure.

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 score0.842
Threshold uncertainty score0.849

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 head0.060
GPT teacher head0.304
Teacher spread0.244 · 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

Citations134
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicEcology and Vegetation Dynamics StudiesFrench-language works237,207