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Record W2607253946 · doi:10.1139/cjfr-2017-0096

Seed production of sugar maple and American beech in northern hardwood forests, New Hampshire, USA

2017· article· en· W2607253946 on OpenAlexvenueno aff
Natalie L. Cleavitt, Timothy J. Fahey

Bibliographic record

VenueCanadian Journal of Forest Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersU.S. Department of AgricultureU.S. Forest ServiceNational Science Foundation
KeywordsMast (botany)BeechHardwoodYellow birchFagaceaeAcornMapleMarshBiologyForestryEcologyGeography

Abstract

fetched live from OpenAlex

Mast seeding is the synchronous production of large seed crops in plant populations and for many tree species is known to be determined by the interaction between weather cues and internal plant resources. We use a 24-year record of seedfall for sugar maple (Acer saccharum Marsh.) and American beech (Fagus grandifolia Ehrh.) across a northern hardwood forest landscape to quantify their masting patterns and explore the relationship between mast years, resources, and weather cues, particularly the difference between summer temperatures in the two years prior to the seedfall year (ΔT). We found clear evidence of masting in these species, and mast years were often coincident in the two species; masting was best predicted by ΔT or ΔT plus previous-year seedfall. We saw no evidence for correspondence of masting in these trees to precipitation cues. A soil calcium addition modified elevation effects on seed production. Clarification of the controls on mast seeding for these important tree species will aid in predicting such resources as mast for wildlife and maple sugar production in northern hardwood forests.

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 score0.805
Threshold uncertainty score0.819

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 studies0.0000.002
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 head0.051
GPT teacher head0.326
Teacher spread0.275 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

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