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Record W2103540458 · doi:10.1139/x09-084

Conifer seed predation in harvested and burned dry Douglas-fir forests in southern British Columbia

2009· article· en· W2103540458 on OpenAlexaffvenueabout
David J. Huggard, André Arsenault

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsVancouver Biotech (Canada)Government of British Columbia
Fundersnot available
KeywordsExclosurePredationSeed predationLitterBiologyNatural regenerationSeedlingDouglas firAgronomyForestryEcologyEnvironmental scienceGrazingBotanySeed dispersalGeographyPopulation

Abstract

fetched live from OpenAlex

Consumption of seeds of Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) and ponderosa pine ( Pinus ponderosa Dougl. ex P. & C. Laws.) was measured in dry Douglas-fir forest at the Opax Mountain Silvicultural Systems site and a nearby burned area as one component of understanding limited natural regeneration in these sites. Seeds were placed in plots surrounded by a physical barrier to invertebrates, enclosed in mesh impermeable to small mammals, covered by litter, or unprotected in clear-cut, partially harvested, uncut, and burned areas and monitored for three several-day sessions in 2 years. Daily survival rates of unprotected seeds of both species were equally low: 0.63 in uncut forest, 0.45 in partially harvested and clear-cut sites, and 0.03 in the burned area. Experimental reductions of coarse woody debris reduced seed predation moderately in patch cuts but not in uncut forest. Litter cover or selective exclosure of ground-dwelling invertebrates reduced seed losses only slightly, whereas small mammal exclosures eliminated most seed loss. Deer mice ( Peromyscus maniculatus Wagner) are likely the most important seed predators. High rates of seed predation in dry Douglas-fir forests likely limit natural regeneration in harvested and burned stands.

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)0.001
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 score0.157
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.252
Teacher spread0.237 · 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

Citations8
Published2009
Admission routes3
Has abstractyes

Explore more

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