MétaCan
Menu
Back to cohort
Record W3121388303

FINANCIAL AND HEALTH COSTS OF PESTICIDE USE IN GROWING CONVENTIONAL AND GENETICALLY MODIFIED POTATOES IN PRINCE EDWARD ISLAND

2004· preprint· en· W3121388303 on OpenAlexaboutno aff
Wiktor Adamowicz, Michele M. Veeman, Elspeth White

Bibliographic record

VenueRePEc: Research Papers in Economics · 2004
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsPesticideAgricultureContext (archaeology)Pesticide applicationGenetically engineeredHealth benefitsAgricultural scienceGenetically modified organismPesticide residueBusinessHuman healthAgricultural economicsBiotechnologyGeographyEconomicsEnvironmental scienceAgronomyBiologyEnvironmental healthMedicine
DOInot available

Abstract

fetched live from OpenAlex

The majority of potato farming in Canada occurs in tightly clustered geographic locations and requires substantial chemical inputs. The possibility of pesticide drift, pesticide residues on food and the effect of pesticides on the environment, leads to interest in quantifying the different effects that pesticides may have on human health and the environment. This study focuses on the potential use of genetically modified potatoes, the associated issue of pesticide residues in the air, and the potential impact of this on the health of farmers, their families, and others in the context of Prince Edward Island. Reductions in costs of potato farming and reduced health costs that may be associated with lower pesticide applications in growing genetically engineered potatoes (NewLeaf, NewLeaf Plus and NewLeaf Pro potatoes, each genetically modified for particular traits), relative to conventional potato growing practices in Prince Edward Island are identified and quantified. It is concluded that the financial benefits from the use of fewer inputs with the modified potatoes are significant while the health benefits associated with reduced exposure to pesticides are relatively small.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.163
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.001
Research integrity0.0000.001
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 head0.046
GPT teacher head0.307
Teacher spread0.261 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicPotato Plant ResearchFrench-language works237,207