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Record W3094099093 · doi:10.14719/pst.2020.7.4.927

Early topping: an alternative to standard topping increases yield in cannabis production

2020· article· en· W3094099093 on OpenAlexaff
Sylvain Gaudreau, Tagnon D. Missihoun, Hugo Germain

Bibliographic record

VenuePlant Science Today · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsToppingCuttingShootProduction (economics)HorticultureBiology

Abstract

fetched live from OpenAlex

In commercial settings, cannabis is generally propagated through cuttings, a process referred in the industry as cloning. Some producers perform either topping or fimming to trigger the production of axillary shoots, which will enhance the number of flowers per plants and thus increase the yield of the cannabis plants. Topping or fimming is generally performed after the cuttings have been transferred to rooting media for two weeks. We have tested a new method to increase the shoot number per plant. The modification of the standard topping method consist of performing the topping on mother plants, prior to taking the cuttings for cloning, and the cuttings are taken one week after the topping is performed. The resulting plantlets develop axillary shoots much faster and the time of production from cuttings to harvesting is decreased by 7-10 days. The method proposed herein requires minimal adjustment to the existing workflow and the plants produce as much as when standard topping is performed. Moreover, this method cuts backs on the production time and nearly two weeks are saved compared to the standard topping procedure since the plantlets do not need to recover after topping. Application of this new procedure results in faster production time and ultimately enhanced productivity.

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)0.003
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 score0.451
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.048
GPT teacher head0.319
Teacher spread0.271 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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