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Record W37960087 · doi:10.3390/plants12213731

Peppermint (Menths piperita)- A local strain from kangra valley of Himachal Pradesh.

2004· article· en· W37960087 on OpenAlexfundno aff
Virendra Singh, Nima W. Megeji, V. K. Kaul, Abilekha Sharma, M L Mehashwari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStrain (injury)GeographyBiology

Abstract

fetched live from OpenAlex

<i>Cannabis sativa</i> L. is cultivated globally for its cannabinoid-dense inflorescences. Commercial preference for sinsemilla has led to the development of methods for producing feminized seeds through cross-pollination of cosexual (masculinized) female plants. Although the induction of cosexuality in <i>Cannabis</i> plants is common, to date, no work has empirically tested how masculinization of female <i>Cannabis</i> plants impacts male flowering, pollen production, pollen fitness, and related life-history trade-offs. Here, we cultivated a population of <i>Cannabis</i> plants (CFX-2) and explored how the route to cosexuality (drought vs. chemical induction) impacted flowering phenology, pollen production, and pollen fitness, relative to unsexual male plants. Unisexual males flowered earlier and longer than cosexual plants and produced 223% more total pollen (F<sub>2,28</sub> = 74.41, <i>p</i> < 0.001), but per-flower pollen production did not differ across reproductive phenotypes (F<sub>2,21</sub> = 0.887, <i>p</i> = 0.427). Pollen viability was 200% higher in unisexual males and drought-induced cosexuals (F<sub>2,36</sub> = 189.70, <i>p</i> < 0.001). Pollen non-abortion rates only differed in a marginally significant way across reproductive phenotypes (F<sub>2,36</sub> = 3.00, <i>p</i> = 0.06). Here, we demonstrate that masculinization of female plants impacts whole-plant pollen production and pollen fitness in <i>Cannabis sativa</i>.

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 categoriesInsufficient payload (model declined to judge)
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 score0.598
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.207
Teacher spread0.190 · 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.

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
Published2004
Admission routes1
Has abstractyes

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