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Record W2154555816 · doi:10.1139/b05-043

Nouvelle méthode de suivi au champ de la sénescence des feuilles de maïs

2005· article· en· W2154555816 on OpenAlexvenueno aff
Laurette Combe

Bibliographic record

VenueCanadian Journal of Botany · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsnot available
Fundersnot available
KeywordsCanopyPlant canopyHorticultureBotanyMathematicsBiology

Abstract

fetched live from OpenAlex

A new method was developed to describe and quantify leaf senescence in a field-grown maize canopy with the purpose of future crop modelling. Leaf shape was analysed using the outlines of over 500 adult leaves from plants grown under diverse culture conditions. Leaf width (λ) taken at regular distances from the ligule (L) allowed the development of leaf-shape equations. These are third-degree polynomials. Thus, four leaf measurements are needed for characterizing leaf shape: maximum width (λm), total length (Lt), width at ligule level (λo), and distance from ligule to the point of maximum width (Lx). Assuming y = (λ/λm) and x = (L/Lt), the shape equation can be written as y = (1 – x)(ax 2 + bx + y o ). Coefficients a and b were estimated from (λo/λm) = y o and (Lx/Lt) = x m . The leaf area between the ligule and any point on the midrib can be obtained by analytically integrating the leaf shape equation. In the present work, degreening was used as the indicator of senescence. Thus, once the shape equation was been defined, easily taken measurements of “green colour” allowed me to calculate the remaining green area by leaf. The vertical profile of the green area on the whole plant could then be described. The time course of green leaf area in a field-grown maize canopy was analysed from silking up to harvest. The farther the leaves were from the ear and the lower they were in the plant, the earlier they lost their green colour. The smallest leaves fully changed colour in 1 week, while this process took 2 weeks for the largest ones. A whole plant lost its green colour in about 5 weeks. Drought caused earlier but not faster leaf degreening. Low plant densities delayed discoloration of the leaves close to the ear, even under drought conditions. The start of senescence in leaves 7–16 was almost simultaneous in all crop conditions, but it could not be determined whether this was due to a threshold effect of the source/sink ratio or to another factor. This method was easy to set up for field studies with maize, but it could be also used for studies on other graminaceous species and some broad-leaved species. It could be extended to describe the physiological functioning over the leaf length and across the vertical profile of the whole plant. Key words: leaf shape, leaf area, leaf senescence, leaf color, maize, plant density, drought.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.987

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 studies0.0000.000
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 head0.033
GPT teacher head0.216
Teacher spread0.183 · 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

Citations9
Published2005
Admission routes1
Has abstractyes

Explore more

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