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Record W2143368652 · doi:10.1139/x06-005

Height growth determinants and adaptation to temperature in pines: a case study of<i>Pinus contorta</i>and<i>Pinus monticola</i>

2006· article· en· W2143368652 on OpenAlexvenueno aff
Isabelle Chuine, Gerald E. Rehfeldt, Sally N. Aitken

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsPinus contortaPhenologyBiologyShootProvenanceBotanyPlant stemHorticulturePinus yunnanensisPinus <genus>

Abstract

fetched live from OpenAlex

In this study we aimed to compare and explain the height growth performance of two contrasting pine species: lodgepole pine (Pinus contorta Dougl. ex. Loud) and western white pine(Pinus monticola Dougl. ex D. Don.). We compiled measurements of total height growth at different ages and shoot elongation phenology realized in several provenance test trials for 109 provenances of lodgepole pine and 54 provenances of western white pine. The response of shoot elongation to temperature was assessed using a phenological model fitted on provenance mean growth curves. Although total height growth followed the same geographic trends in both species, the response of shoot elongation to temperature was different between the two, with few (lodgepole pine) or no differences among provenances (western white pine) from diverse geographic regions. The temperature for which potential cell growth rate is 50% was 10.8 ± 0.13 °C (mean ± standard error) for western white pine compared to 5.26 ± 0.075 °C for lodgepole pine. Phenology did not explain growth performance differences among geographical regions in both species, which instead were explained by differences in the number of internodes set the preceding summer; provenances originating from stressful environments produced the fewest internodes, possibly due to reallocation of carbohydrates to stress resistance.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.762

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 science0.0000.000
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 head0.020
GPT teacher head0.270
Teacher spread0.250 · 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

Citations45
Published2006
Admission routes1
Has abstractyes

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