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Record W1863168108 · doi:10.1139/b11-059

Anatomy and photochemical behaviour of Mediterranean <i>Cistus incanus</i> winter leaves under natural outdoor and warmer indoor conditions

2011· article· en· W1863168108 on OpenAlexvenueno aff
Veronica De Micco, Carmen Arena, Luca Vitale, Giovanna Aronne, A. Vírzo De Santo

Bibliographic record

VenueBotany · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsnot available
Fundersnot available
KeywordsPhotoprotectionBiologyPhotoinhibitionMediterranean climateBotanyPhotosynthesisHorticultureEcologyPhotosystem II

Abstract

fetched live from OpenAlex

Plants in Mediterranean environments can adopt photoprotective mechanisms to cope with winter temperatures, thus avoiding photoinhibition. An increase in global temperature is expected in the future as a consequence of climate change. The aims of this work were (i) to analyse anatomy and photochemical activity in winter leaves of Cistus incanus L., to identify characters having a potential role in photoprotection under natural winter conditions, and (ii) to evaluate the effect of higher temperature on such traits. Leaves from plants grown in a greenhouse (indoor) were compared with those from plants grown outdoors on the basis of anatomical and photochemical traits, including indexes evaluating the exposure of chloroplasts at the cell surface. Leaves developed outdoors were characterized by anatomical traits related to the optimization of light harvesting and to gas-exchange control. Such traits were accompanied by a higher pigment content and an increase in the thermal dissipation of excess absorbed light. Indoor leaves showed physiological and morphoanatomical adjustments addressed to invest more of absorbed light in photochemistry. Overall, our analysis showed that (i) photoprotection in C. incanus relies on adaptive traits at both the morphoanatomical and physiological level and (ii) C. incanus leaves seem to be able to acclimatize to warmer winters.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.240
Teacher spread0.217 · 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

Citations37
Published2011
Admission routes1
Has abstractyes

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