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Record W7117111073 · doi:10.3381/tobsci-d-24-00001

PROMOTING EARLY FLOWERING IN FLUE-CURED TOBACCO CULTIVARS USING ETRIDIAZOLE AND HIGH-INTENSITY LIGHT—RESPONSE FROM 2 TYPES OF TRAYS

2024· article· W7117111073 on OpenAlexaff
G.A. Amankwa, M. AL-Amery, A.D. Shearer, Anna Thiessen, C. Saude, M.D. Richmond

Bibliographic record

VenueTobacco Science · 2024
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsOntario Tobacco Research Unit
Fundersnot available
KeywordsTrayCultivarSeedingFungicidePellets

Abstract

fetched live from OpenAlex

Breeding for improved varieties that are in line with the market-preferred traits takes many years using traditional breeding methods. Methods that can accelerate the breeding process reduce the time required for cultivar development, release, and commercialization. This study evaluated the effect of tray cell size on the potential for using high-intensity light and etridiazole fungicide (Truban® 25% EC) application as a means of inducing early flowering response in tobacco seedlings for breeding purposes. Tobacco seedlings were grown in expanded polystyrene (Styrofoam) trays of 2 different sizes, which were floated on a solution of etridiazole 20 times the recommended rate of 0.0381 g/L, under high-intensity light (800 W, Fusion Bright 400 Super HPS high-pressure sodium bulb). The results revealed that the tray with larger cell volume, the 128-cell tray, allowed for improved plant growth due to a better root environment giving over 3.5 times more floral initiation and flowering as compared to the tray with small cell size, the 288-cell tray. The large cell tray also provided plants with the better outcome during subsequent seed production. The current study highlights the potential advantages of using this system in accelerated breeding for the successful development and release of pure line cultivars by allowing multiple generations to be achieved within 1 year.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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