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Record W4291010399 · doi:10.5558/tfc2022-005

A review of climate change effects on the regeneration dynamics of balsam fir

2022· review· en· W4291010399 on OpenAlexaffvenueabout
Joe Collier, David A. MacLean, Loïc D’Orangeville, Anthony R. Taylor

Bibliographic record

VenueThe Forestry Chronicle · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBalsamAbies balsameaClimate changeRegeneration (biology)GerminationSeedlingEnvironmental scienceBiologyEcologyForestryBotanyGeography

Abstract

fetched live from OpenAlex

Balsam fir (Abies balsamea) is one of the most abundant softwood species in eastern Canada but is projected to be adversely affected by climate change. Balsam fir decline could occur due to a combination of reduced germination and regeneration, lower growth and competitive ability, and higher rates of mortality. However, tree regeneration represents one of the most vulnerable stages to climate-induced stress. In this paper, we synthesize potential and observed effects of climate change on balsam fir regeneration. Recent studies have shown no detrimental effects of increased temperatures on either germination or seedling growth of balsam fir, but clear deleterious effects of decreased water availability. Balsam fir seeds require 28–60 days of cold stratification to become germinable, and such conditions should still be met under climate change across most of the species’ range. Sampling along a north-south climatic gradient throughout the Acadian Forest Region of eastern Canada indicated effects are complex and do not suggest a clear decline under warmer, drier conditions for the species. Thus, effects of global warming on balsam fir may be more gradual than projected in modeling studies and occur primarily via reduced competitive ability and/or higher mortality in overstory trees, rather than regeneration failure.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.027
GPT teacher head0.265
Teacher spread0.237 · 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 designOther design
Domainnot available
GenreReview

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

Citations13
Published2022
Admission routes3
Has abstractyes

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