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Record W2109496506 · doi:10.1139/cjfr-2013-0022

Age and size effects on seed productivity of northern black spruce

2013· article· en· W2109496506 on OpenAlexafffundvenueabout
Jayme Nicole Viglas, Carissa D. Brown, Jill F. Johnstone

Bibliographic record

VenueCanadian Journal of Forest Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Saskatchewan
FundersPacific Northwest Research StationNatural Sciences and Engineering Research Council of CanadaU.S. Forest Service
KeywordsBlack spruceBasal areaTaigaProductivitySeedlingForestryBiologyBorealGeographyEcologyAgronomy

Abstract

fetched live from OpenAlex

Slow-growing conifers of the northern boreal forest may require several decades to reach reproductive maturity, making them vulnerable to increases in disturbance frequency. Here, we examine the relationship between stand age and seed productivity of black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) in Yukon Territory and Alaska. Black spruce trees were aged and surveyed for cone production and seed viability across 30 even-aged stands ranging from 12 to 197 years old. Logistic regression indicated that individual trees had a ∼50% probability of producing cones by age 30 years, which increased to 90% by age 100 years. Cone and seed production increased steadily with age or basal area at both the tree and stand level, with no evidence of declining seed production in trees older than 150 years. Using published seed:seedling ratios, we estimated that postfire recruitment will be limited by seed availability in stands for up to 50 years (on high-quality seedbeds) to 150 years (low-quality seedbeds) after fire. By quantifying these age and seed productivity relationships, we can improve our ability to predict the sensitivity of conifer seed production to a range of disturbance frequencies and thus anticipate changes in boreal forest resilience to altered fire regime.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.345
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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)0.0000.000

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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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

Citations82
Published2013
Admission routes4
Has abstractyes

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