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Record W2279825604 · doi:10.1139/cjb-2015-0166

A quantitative analysis of seed dormancy and germination in the winter annual weed <i>Sinapis arvensis</i> (Brassicaceae)

2016· article· en· W2279825604 on OpenAlexvenueno aff
Elias Soltani, Carol C. Baskin, Jerry M. Baskin, Afshin Soltani, Farshid Ghaderi‐Far, E Zeinali

Bibliographic record

VenueBotany · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDormancyGerminationBiologySinapisWeedAgronomyBrassicaceaeHorticultureSeed dormancyBotanyBrassica

Abstract

fetched live from OpenAlex

The aims of this study were to determine the effects of burial on germination and longevity, and of water stress and temperature on germination and dormancy induction of the weed Sinapis arvensis L. During exposure to the high temperatures of summer, seeds buried in the field became nondormant, but low water potential and supra-optimal temperatures (constant not alternating) induced them into secondary dormancy. The threshold temperature for dormancy induction (TTDI) was about 19 °C when water was not limiting germination, and it decreased with a slope of 10 °C per MPa as water potential decreased. Seeds had minimum dormancy (D min ) when T &lt; TTDI, and D min decreased by 81.5% per MPa increase in water potential. Dormancy induction increased linearly with a slope of 13.23% for each additional centimetre of burial depth from 1.0 to 5.19 cm. Dormancy was induced to its highest level (96%) in seeds buried at a depth of ≥5.19 cm; the remaining seeds were dead or were presumed to be dead Sinapis arvensis can form a persistent soil seed bank, and either water stress or conditions associated with increased burial depth can promote induction of secondary dormancy in the seeds.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.167

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 studies0.0000.000
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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