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Record W2290758461 · doi:10.2298/abs150311099z

Seasonal variation of leaf ecophysiological traits of Iris variegata observed in two consecutive years in natural habitats with contrasting light conditions

2015· article· en· W2290758461 on OpenAlexaff
Uroš Živković, Danijela Miljković, Nataša Barišić Klisarić, Aleksej Tarasjev, Stevan Avramov

Bibliographic record

VenueArchives of Biological Sciences · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsInstitute for Biological Sciences
FundersScience and Engineering Research BoardMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsBiologyHabitatGrowing seasonBotanyVegetation (pathology)Chlorophyll aSpecific leaf areaChlorophyllWoodlandEcologyPhotosynthesisHorticulture

Abstract

fetched live from OpenAlex

The amount and pattern of individual phenotypic responses to seasonal changes in environmental conditions were determined in clones of Iris variegata growing in differing light habitats. For the purpose of the study, 97 clonal plants of the rhizomatous herb I. variegata that experienced different light conditions in their two native habitats were selected: one along the top and slope of sand dunes and one in woodland understories. Two fully expanded leaves that had developed during spring, summer and fall in two consecutive years were sampled from each of these clones. Six leaf traits affecting the photosynthetic rate of a plant ? morphological (specific leaf area), anatomical (stomatal density) and physiological (total chlorophyll concentration, chlorophyll a/chlorophyll b ratio, carotenoid concentration, chlorophyll a/carotenoid ratio) exhibited significant plastic responses in the two different light habitats. To test whether these traits differ between exposed and shaded habitats as well as during different vegetation periods, we used the repeated model analysis of variance (ANOVA). Results of the repeated ANOVA revealed statistically significant effects of year, habitat and period of vegetation season. Patterns of changes during growing seasons were year-specific for almost all analyzed traits.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.370

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 studies0.0000.001
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 head0.083
GPT teacher head0.261
Teacher spread0.178 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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