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Record W2127411049 · doi:10.1071/fp12194

The effect of aerobic soil conditions, soil volume and sowing date on the development of four tropical rice varieties grown in the greenhouse

2012· article· en· W2127411049 on OpenAlexfundno aff
Benoît Clerget, Crisanta Sunio Bueno

Bibliographic record

VenueFunctional Plant Biology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
FundersChinese Academy of SciencesChinese Academy of Agricultural SciencesMcMaster University
KeywordsSowingPanicleBiologyAgronomyAnthesisGreenhouseCultivar

Abstract

fetched live from OpenAlex

Rice has generally been reported to yield less in aerobic soil conditions than in flooded soil conditions, in association with delayed anthesis and a reduction in height. A greenhouse experiment was conducted and repeated twice with four rice varieties grown in either flooded or nearly-saturated aerobic soil, in either large or small pots. The rate of leaf appearance was recorded weekly until heading time, when plants were harvested for shoot and root biomass. The kinetics of leaf appearance was generally trilinear with longer phyllochrons in the May sowing. Pot size had only a small effect whereas aerobic soil conditions slowed down the rate of leaf initiation, which consequently delayed panicle initiation and heading date and thus increased the duration of the tillering phase and finally the number of tillers. Surprisingly, the four varieties showed a significant response to sowing date, especially IR72, which headed 21 days later and after the expansion of two more leaves in the May sowing. The aerobic soil conditions and sowing date have thus changed the rate of development of rice plants and this trait has appeared to be the initial link of a chain of consequences in a series of traits known to be affected by these factors.

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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.215
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 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

Citations16
Published2012
Admission routes1
Has abstractyes

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