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Record W2120632318 · doi:10.1139/x05-007

Photosynthesis of birch (<i>Betula pendula</i>) is sensitive to springtime frost and ozone

2005· article· en· W2120632318 on OpenAlexvenueno aff
Elina Oksanen, V. Freiwald, Nadezhda A. Prozherina, Matti Rousi

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
Fundersnot available
KeywordsPhotosynthesisBetula pendulaStomatal conductanceFrost (temperature)OzoneBotanyHorticultureBetulaceaeBetula pubescensRuBisCOChemistryGrowing seasonBiologyMeteorology

Abstract

fetched live from OpenAlex

Impacts of springtime frost and ozone enrichment, alone and in combination, on six birch (Betula pendula Roth) genotypes regenerated from a naturally occurring birch stand in southeastern Finland were studied. The seedlings were exposed to 65 ppb ozone (AOT40 (accumulated over a threshold of 40 ppb) exposure of 10.7 ppm·h) over 62 d in climate chambers, simulating spring conditions, and to –2 °C over two consecutive nights 33 d after the start of the experiment. The plants were measured for net photosynthesis, stomatal conductance, and concentrations of photosynthetic pigments, Rubisco, soluble proteins, carbohydrates, and macronutrients. Frost treatment caused a rapid 60%–77% decline in net photosynthesis and stomatal conductance rates. Recovery of net photosynthesis from frost was not complete during the subsequent 14 d, mainly because of impaired light capture through significant pigment loss and structural injuries. Concomitant ozone enrichment exacerbated the negative effect of frost on pigments and stomatal conductance. Both frost and ozone caused nutrient imbalance and increase in soluble proteins in leaves, whereas metabolism of carbohydrates was disturbed only when ozone was present. Responses to ozone and frost varied greatly among the genotypes, suggesting that there is a high capacity within the birch population to adapt to climate change through "preadapted" individuals.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.038
GPT teacher head0.268
Teacher spread0.230 · 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

Citations38
Published2005
Admission routes1
Has abstractyes

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