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Record W2151198324 · doi:10.1139/cjfr-2013-0481

Soil characteristics mediate the distribution and response of boreal trees to climatic variability

2014· article· en· W2151198324 on OpenAlexaffvenueabout
Sylvie Gewehr, Igor Drobyshev, Frank Berninger, Yves Bergeron

Bibliographic record

VenueCanadian Journal of Forest Research · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
Fundersnot available
KeywordsBlack spruceTaigaPrecipitationGrowing seasonBasal areaEnvironmental scienceBorealFrost (temperature)Picea abiesDendrochronologyClimate changeAgronomyBiologyBotanyEcologyGeography

Abstract

fetched live from OpenAlex

We studied the effects of the soil organic layer (SOL) accumulation on growth and distribution of black spruce (Picea mariana (Mill.) BSP) and trembling aspen (Populus tremuloides Michx.) within the Quebec Clay Belt. At the landscape scale, spruce was present over a much larger gradient in SOL thickness (∼1 to 100 cm) than aspen (∼1 to 30 cm). For trees between 60 and 100 years old, SOL thickness had no effect on the basal area increment (BAI) of spruce but showed a strong and negative correlation with BAI in aspen. Radial growth of black spruce was favored by higher precipitation in June of the previous growing season, higher temperatures in early winter and in spring, and by low temperatures in summer. SOL thickness had statistically significant but moderate effects on the climate–growth relationships in spruce, apparently affecting root insulation during the dormant period and water availability during the growing period. In aspen, current-year June temperature was the most important factor positively correlated with growth. The SOL thickness affected the relationship between the aspen growth and (i) January temperature and (ii) June–August monthly drought code. We predict that the response of black spruce to climate change should be rather uniform across the study region, while the response of aspen is likely to be strongly mediated by SOL thickness.

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)0.011
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.010
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)0.0000.000

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 head0.031
GPT teacher head0.284
Teacher spread0.253 · 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.

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

Citations43
Published2014
Admission routes3
Has abstractyes

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