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Record W2140374054 · doi:10.1093/treephys/tpq097

Why do genotypes of Picea glauca differ in their growth response to elevated CO2?

2011· article· en· W2140374054 on OpenAlexafffund
Jumei Zhang, Erin E. Mycroft, G. W. Adams, E. G. Reekie

Bibliographic record

VenueTree Physiology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsJ. D. Irving (Canada)Acadia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRelative growth rateBiologyCanopyPhotosynthesisSpecific leaf areaGrowth rateBotany

Abstract

fetched live from OpenAlex

Meta-analyses reveal that fast-growing species have a greater growth response to elevated CO(2) than slow-growing species. It is unknown whether this is a direct response or whether inter-specific differences in growth are simply correlated with other physiological or morphological differences among species that affect the growth response to CO(2). Here we use intra-specific variation in Picea glauca to examine the mechanistic basis for this relationship. Relative growth rate (RGR) of 29 genotypes grown at ambient (370 µl l(-1)) or elevated (740 µl 1(-1)) CO(2) was measured. Physiological and morphological traits describing differences in allocation, canopy structure, stomatal function and photosynthesis were determined. Most variation in RGR (74%) was explained by traits associated with canopy structure. Although there was a strong correlation between RGR(740) and RGR(370), we found no evidence that genotypes that grew fast at ambient CO(2) had a greater relative growth response to CO(2). Given that the pattern found at the intra-specific level differed from that reported at the inter-specific level, our results suggest that RGR per se does not affect the growth response to CO(2). Rather, the CO(2) growth response is determined by traits that may or may not be correlated with RGR.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.208
Teacher spread0.185 · 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 designBench or experimental
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

Citations4
Published2011
Admission routes2
Has abstractyes

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