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Évolution saisonnière de la composition foliaire de<i>Stipa tenacissima</i>L. en éléments minéraux et en fibres pariétales

2008· article· fr· W1997603990 on OpenAlexaff
Zoheir Mehdadi, Zineddine Benaouda, Ali Latrèche, Hachemi Benhassaini, Slimane Belbraouet

Bibliographic record

VenueActa Botanica Gallica · 2008
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicBotanical Research and Applications
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsPotassiumCellulosePhosphorusComposition (language)ChemistryStatistical analysisBotanyLigninAnimal scienceBiologyAgronomyBiochemistryMathematics

Abstract

fetched live from OpenAlex

The leaf composition of Stipa tenacissima L. in mineral elements and parietal fibers presents a seasonal quantitative variation confirmed by statistical analysis. A principal components analysis showed two essential groups of biochemical variables correlated in spring and summer, translating so the influence of these two seasons on their reworking in the alfa leaves. The first group correlated at the summer seems, making up a biochemical strategy of adaptation to the conditions of summer pause. It is represented by lignin, cellulose, total fibers, potassium and copper. The second group correlated at spring, biological phase where the conditions of growth are optimal, is consisted of pectins, hemicelluloses, nitrogen, magnesium, iron and phosphorus. Comparatively to the mineral matter's weak rate, the parietal fibers, particularly the cellulose and hemicelluloses, are the major compounds in alfa leaves.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.298
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2008
Admission routes1
Has abstractyes

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