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Record W3160698765 · doi:10.33002/jelp001.02

ANALYSIS OF INDIAN AND CANADIAN LAWS REGULATING THE BIOPESTICIDES: A COMPARISON

2021· article· en· W3160698765 on OpenAlexafffundabout
Hasrat Arjjumend, Konstantia Koutouki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsUniversité de Montréal
FundersMitacs
KeywordsBiopesticideBiotechnologyCrop protectionSustainabilityBusinessNatural resource economicsPesticideBiologyAgroforestryEcologyEconomics

Abstract

fetched live from OpenAlex

An excessive use of toxic plant protection chemicals has irreversibly damaged the soil biology of agroecosystems, resulting in a substantial decline of productivity. Biocontrol agents, especially microbial biopesticides, are seen as one of the key solutions to overcome toxicity and pest resistance issues. Biopesticides are defined as mass-produced agents manufactured from living microorganisms or natural products used for the control of pests. Laws to regulate biopesticides both in India and Canada need to be analysed from the perspectives of trade facilitation, ease of business, proliferation of green technologies and products, and the sustainability and revitalization of soil biology. Registration of new biopesticides for its manufacturing, trade, import, storage, transport, disposal and safety is discussed from the point of view of the legal barriers imposed on the production process and trade. Having compared laws of both countries, authors offer recommendations for legal reform.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0120.020
Science and technology studies0.0050.002
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.009
GPT teacher head0.255
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations4
Published2021
Admission routes3
Has abstractyes

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