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Record W2799276504 · doi:10.1139/cjps-2018-0038

Vegetative propagation of cannabis by stem cuttings: effects of leaf number, cutting position, rooting hormone, and leaf tip removal

2018· article· en· W2799276504 on OpenAlexaffvenue
Deron Caplan, Jonathan Stemeroff, Mike Dixon, Youbin Zheng

Bibliographic record

VenueCanadian Journal of Plant Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCuttingHorticultureBiologyWillowBotanyVegetative reproductionAuxin

Abstract

fetched live from OpenAlex

This study evaluated the influence of several factors and their interactive effects on the propagation success of stem cuttings of cannabis (Cannabis sativa L.). Factors included (i) leaf number (two or three), (ii) leaf tip removal (one-third of leaf tips removed), (iii) basal/apical position of stem cutting on the stock plant, and (iv) rooting hormone [0.2% indole-3-butyric (IBA) acid gel or 0.2% willow (Salix alba L.) extract gel]. Cuttings were placed in a growth chamber for twelve days and then assessed on their rooting success rate and root quality using a relative root quality scale. The IBA gel delivered a 2.1× higher rooting success rate and 1.6× higher root quality than the willow extract. Removing leaf tips reduced rooting success rate from 71% to 53% without influencing root quality. Cuttings with three leaves had 15% higher root quality compared with those with two, but leaf number did not influence rooting success rate. Position of cutting had little effect on rooting success or quality. To achieve maximum rooting success and root quality, cuttings from either apical or basal positions should have at least three fully expanded uncut leaves and the tested IBA rooting hormone is preferred to the willow-based product.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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

Citations69
Published2018
Admission routes2
Has abstractyes

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