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Record W1971291955 · doi:10.1139/x02-153

Pre-dispersal seed predation of white spruce cones in logged boreal mixedwood forest

2003· article· en· W1971291955 on OpenAlexfundvenueaboutno aff
Susan H Peters, Stan Boutin, S. Ellen Macdonald

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersDirectorate for Biological SciencesUniversity of AlbertaAlberta-Pacific Forest Industries
KeywordsBiologyTaigaPredationSeed predationThinningSeed dispersalConifer coneCrown (dentistry)ForestryHorticultureBotanyBiological dispersalEcologyPopulationGeography

Abstract

fetched live from OpenAlex

Predation of white spruce (Picea glauca (Moench) Voss) cones by red squirrels (Tamiasciurus hudsonicus Erxleben) was quantified in the mixedwood boreal forest of Alberta in cutblocks with seed tree retention and in adjacent uncut forest, during 3 years with varying levels of cone crop (1998, 1999, 2000). Percent cone loss was quantified by comparison of paired pre- and post-caching photographs of tree crowns. Cone loss from seed trees in cutblocks was significantly lower than from control trees in adjacent uncut forest (48.5 vs. 54.9%). Although the number of cones produced per tree declined by 42% and the percentage of trees producing cones declined by approximately 48% between 1998 and 2000, there was no corresponding increase in the percentage of cones harvested by squirrels. Percent cone loss was significantly lower from single seed trees in cutblocks, as compared with seed trees left in patches of more than 20 trees (33.4 vs. 50.5%). Cone predation significantly reduced the amount of seed available for natural regeneration using a seed tree system. Although blowdown may be reduced if seed trees are left in patches, leaving white spruce seed trees as singles in cutblocks may reduce the level of cone predation.

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 score0.800
Threshold uncertainty score0.985

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.305
Teacher spread0.271 · 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

Citations26
Published2003
Admission routes3
Has abstractyes

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