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Precision Management Practices for Legal Cultivation of Cannabis (Cannabis sativa L.)

2020· book-chapter· en· W3093616775 on OpenAlexaff
Aitazaz A. Farooque, Farhat Abbas

Bibliographic record

VenueAdvances in environmental engineering and green technologies book series · 2020
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Management Techniques
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsCannabis sativaCannabisPowdery mildewAgricultureBiotechnologyBusinessNutrientNutrient managementScope (computer science)Agricultural scienceDisease managementEnvironmental scienceAgricultural engineeringEngineeringAgroforestryMedicineAgronomyBiologyDiseaseComputer scienceEcologyHorticulture

Abstract

fetched live from OpenAlex

Cannabis (Cannabis sativa L.) growers worldwide lack reliable and research-based information about precision management practices (PMP) of cannabis. The history, legal framework, and PMP for cultivation of cannabis have been reviewed with special emphasis on water management, nutrient management, and disease control for optimum cannabis production. The aim is to provide guidelines for precision farming of cannabis to meet fibrous and medicinal needs of the humankind. Therefore, the scope of this chapter is for the potential of hemp cultivation to meet industry needs of fiber and medicine. Methods of irrigation scheduling, nutrient applications, and keeping greenhouse hygienically clean for disease-free (i.e., powdery mildew) hemp production are discussed. Reviewed and recommended application rates of irrigation and nutrients, and environment controls have been tabulated. Chemical, biological, and physical controls of PM control and crop input requirements for disease-free cultivation of hemp are presented.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.762

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 communication0.0000.001
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 head0.007
GPT teacher head0.197
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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