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Biosafety information management systems. A comparative analysis of the regulatory systems in Canada, Argentina, and Chile

2000· article· en· W4245370428 on OpenAlexaffabout
Jason Flint, Lionel Gil, Javier Verastegui, Carlos E. Irarrázabal, Juan Dellacha

Bibliographic record

VenueElectronic Journal of Biotechnology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsBiosafetyBiotechnologyBiologyEnvironmental planningRisk analysis (engineering)Environmental resource managementBusinessGeographyEnvironmental science

Abstract

fetched live from OpenAlex

CamBioTec, a Canadian-Latin American Network promoting the safe and effective use of agricultural and environmental biotechnology, undertook an analysis of the current capacities of Argentina, Chile and Canada with respect to the management of information related to assessment and approval of products of modern biotechnology/ genetically engineering. This report is based on data obtained during a number of interviews and institutional visits conducted during August 1998 and includes: an overview of current regulatory policy, identification of key human resources and authorities, analysis of information management capacity, recommendations for capacity building, and descriptions of relevant international initiatives. Canada has a regulatory system in place that is respected throughout the world for its ability to insure high-quality agricultural biotechnology products that meet international human and environmental health and safety standards. Argentina is recognized as leader among Latin American countries in the regulation of biotechnology products. Chile is a well-known center of genetic diversity for a number of plant species but with very little in the way of biosafety regulation. Together these countries represent a broad spectrum of technical experience, regulatory policy, and agricultural interests.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.191
Teacher spread0.184 · 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

Citations5
Published2000
Admission routes2
Has abstractyes

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