MétaCan
Menu
Back to cohort
Record W2896719261 · doi:10.1139/cjfr-2018-0209

Position of cones within cone clusters determines seed survival in black spruce during wildfire

2018· article· en· W2896719261 on OpenAlexaffvenue
Tadeusz B. Splawinski, David F. Greene, Sean T. Michaletz, Sylvie Gauthier, Daniel Houle, Yves Bergeron

Bibliographic record

VenueCanadian Journal of Forest Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité du Québec à MontréalNatural Resources CanadaCanadian Forest ServiceUniversity of British ColumbiaMinistère des Ressources naturelles et des ForêtsOuranosUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsBlack spruceCrown (dentistry)Atmospheric sciencesEnvironmental scienceCanopyFire regimeEcologyBiologyGeologyTaigaMaterials science

Abstract

fetched live from OpenAlex

It has recently become clear that the regeneration density of serotinous species within a burned area declines with local fire intensity. It is assumed that this occurs because variation in local fire intensity leads to variation in incident heat fluxes and, ultimately, seed necrosis. We argue here that this same relationship between incident heat flux and seed necrosis is important at the scale of individual plant crowns. Using Picea mariana (Mill.) B.S.P. (black spruce), we show that postfire seed viability increases with crown height, depth into the crown, and angle from wind direction (with the windward side enjoying greater viability). All three effects are what one would expect given the physics of buoyant plumes, interactions of moving fire lines with wake flow around cylinders, and heat transfer in porous bodies such as a tree crown. We conclude by discussing the broader consequences of cone cluster size and global change on regeneration in serotinous species.

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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.020
GPT teacher head0.273
Teacher spread0.252 · 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

Citations23
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicFire effects on ecosystemsFrench-language works237,207