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Record W2612068040 · doi:10.1515/sg-2010-0020

Clonal Variation in Acorn Production and its Effect on the Effective Population Size in a Quercus acutissima Seed Orchard

2010· article· en· W2612068040 on OpenAlexafffund
Kyu‐Suk Kang, C. S. Kim, Y. A. El-Kassaby

Bibliographic record

VenueSilvae genetica/Silvae Genetica · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsWestern Forest ProductsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsAcornBiologyFecunditySeed orchardPopulationOrchardAnimal scienceBotanyHorticultureDemography

Abstract

fetched live from OpenAlex

Abstract Acorn production was surveyed for eight consecutive years (2000-2007) in a 94-clone Sawtooth oak (Quercus acutissima) seed orchard established in 1992. Acorn production commenced in 2000 and peaked in 2005 and was characterized by a 3-4 years interval. Sixty out of the orchard’s 94 clones were consistent producers across the study period. Acorn production’s Pearson productmoment and Spearman rank correlation coefficients were significant and consistently positive over the eight years study period. Parental cumulative reproductive output, represented by parental balance curves, slightly varied among mast years and showed steady improvement (less distortion) over years. Effective population size (N p ) was high in moderate and good acorn production years; however, departure from clonal equal contribution was observed throughout the study period. Parental effective population size was estimated under various scenarios of male fecundity (pollen production is: 1) proportional to clone size, 2) equal to female contribution, and 3) equal across all clones) resulted in high N p and low group co-ancestry under equal male fecundity scenario while moderate N p size and group co-ancestry were observed when male fecundity was assumed to be proportional to clone size (i.e., ramet number).

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 score0.330
Threshold uncertainty score0.986

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.232
Teacher spread0.227 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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