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Record W3092160698 · doi:10.1111/cjag.12257

Determinants of the grant lag and the surrender lag of horticultural crop plant breeders’ rights applications: Survival analysis with competing risks

2020· article· en· W3092160698 on OpenAlexaffvenueabout
Ting Meng, Richard Carew, Wojciech J. Florkowski

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSurrenderInefficiencyCertificateAgricultural scienceCropLagAgency (philosophy)BusinessAgricultural economicsActuarial scienceEconomicsPolitical scienceForestryLawGeographyBiologyMathematicsComputer scienceSociology

Abstract

fetched live from OpenAlex

Abstract Obtaining a Plant Breeders’ Rights (PBR) certificate provides horticultural institutions the exclusive right to produce and reproduce new varieties, which directly motivate new plant variety innovations. This study investigates how the grant and surrender lag of PBR certificates is influenced by the crop type and applicant characteristics using the Canadian Food Inspection Agency horticultural crop PBR application data for the period 1992–2014. Results from the Fine and Gray subdistribution hazard model reveal that the grant lag and surrender lag of Canadian PBR applications significantly vary by the country of origin of the applicant, whether the applicant is a public institution or private company/individual, horticultural crop types, and the decades when applications are filed. The policy implications of the results provide useful information to stakeholders of the Canadian Plant Breeders’ Rights System regarding how the lifespan of PBRs is influenced by crop type and applicant characteristics.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.086
GPT teacher head0.173
Teacher spread0.087 · 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 designObservational
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

Citations2
Published2020
Admission routes3
Has abstractyes

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