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Record W1990836102 · doi:10.1139/x03-136

Seasonal variation in physiological characteristics of two silver birch clones in the field

2003· article· en· W1990836102 on OpenAlexvenueno aff
Johanna Riikonen, Elina Oksanen, P. Peltonen, Toini Holopainen, Elina Vapaavuori

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
Fundersnot available
KeywordsRuBisCOPhotosynthesisBiologyStomatal conductanceBetula pendulaBotanyGrowing seasonHorticultureNutrientEcology

Abstract

fetched live from OpenAlex

Seasonal changes in growth, photosynthesis, and related biochemical properties and leaf structure were determined for two clones (4 and 80, 20 trees per clone) of 7-year-old Betula pendula Roth trees during the growing season of 1998. Differences between the two genotypes were determined to characterize the physiological traits that might affect growth and productivity and that might differ between the genotypes. Net photosynthesis of the short shoot leaves varied between 11 and 15 µmol·m –2 ·s –1 and decreased only slightly towards the end of the summer. However, our results showed more marked decreases in the amount of Rubisco (ribulose biphosphate carboxylase/oxygenase) and leaf N and increases in the total leaf, palisade and spongy layer thickness, chloroplast and starch grain size, and diameter of plastoglobuli in both clones in response to leaf ageing and changes in growth environment. Height and biomass were greater in clone 80 than in clone 4. This was related to slightly more efficient net photosynthesis and higher stomatal conductance and density as well as higher activity of Rubisco and content of foliar nutrients (other than N). We conclude that clone 80 is characterized by faster gas exchange, higher Rubisco activity, stomatal conductance, and density, and earlier leaf ageing, which may be related to the higher ozone sensitivity determined previously in pot experiments with younger saplings.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.173
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.001
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 head0.054
GPT teacher head0.301
Teacher spread0.247 · 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

Citations21
Published2003
Admission routes1
Has abstractyes

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