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Record W2572752310 · doi:10.18174/375668

Potential food hazards from organic greenhouse horticulture

2016· preprint· en· W2572752310 on OpenAlexaff
Beatrix Alsanius, Martine Dorais, Orla Doyle, Florin Oancea, D. Spadaro, R.J.M. Meijer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsNational Association of Friendship Centres
Fundersnot available
KeywordsGreenhouseEnvironmental scienceCropProduction (economics)Crop productionOrganic farmingHorticultureAgricultural engineeringAgronomyAgricultureEngineeringEconomicsBiologyEcology

Abstract

fetched live from OpenAlex

Potential food hazards from organic greenhouse horticultureTo meet the Global Challenges for Humanity, sustainable and environmentally sound approaches to crop production are critical tools.Organic production systems are characterised by the wise use of crop input resources.Despite the lack of supporting medical evidence (Smith-Spangler et al., 2012) or evidence of acute pesticide concentrations in non-organic conventional produce (EFSA, 2014), organically produced plant foods are often assumed to be safer and to have stronger health promoting properties than produce produced in conventional or integrated production systems.However, the narrow boundary between animal and organic crop husbandry, coupled with the increasing use of recycled materials as a source of plant nutrients and the application of permitted plant protection products to organic crops pose risks to human health.Awareness of these hazards is low.This factsheet describes the critical hazards in organic greenhouse horticulture (OGH) crop production and identifies the crucial knowledge gaps.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.182
Teacher spread0.173 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

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