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Record W2787162844 · doi:10.5539/jas.v10n3p344

Seasonal Variation of Chlorophyll and Carotenoids in Leaves of Hancornia speciosa in Three Central Areas of the Cerrado of the State of Tocantins, Brazil

2018· article· en· W2787162844 on OpenAlexvenueno aff
Rafael José de Oliveira, José Expedito C. da Silva, Davi Borges das Chagas

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and biological studies
Canadian institutionsnot available
Fundersnot available
KeywordsCarotenoidChlorophyllChlorophyll bChlorophyll aBiologyPhenologyBotanyDeciduousHorticultureWet seasonEcology

Abstract

fetched live from OpenAlex

This study aimed to evaluate the seasonal variation in concentrations of chlorophyll a, b, total chlorophyll and carotenoids in leaves of Hancornia speciosa, Gomes, during the periods of the year, relating them to the main phenological events, periods (rainy, dry and transitions) and populations evaluated. The survey was performed in three sites and the spatio-temporal analysis divided into four periods (rainy, rainy-drytransition, dry and dry-rainy transition), with 10 replicates (matrix plants). The data were collected in average intervals of 33 days from October 2014 to April 2017. The extraction and calculation of the chlorophyll and carotenoid contents of the leaves were expressed in mg/g DM, according to the equations of Arnon (1949) and Lichtenthaler (1987). There was a significant difference between the periods and sites analyzed for all pigments and their relationships. There was a greater amount of chlorophyll a than chlorophyll b; this difference was greater in the dry period. In the rainy period, we found a greater amount of total chlorophyll, carotenoids and total chlorophyll/carotenoid ratio. The behavior for the species follows that already observed for deciduous plants, closely related to water availability.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.765

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 studies0.0000.002
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 head0.006
GPT teacher head0.189
Teacher spread0.182 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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