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Record W2109821596 · doi:10.1139/x05-044

Atmospheric emissions of methyl isothiocyanate and chloropicrin following soil fumigation and surface containment treatment in bare-root forest nurseries

2005· article· en· W2109821596 on OpenAlexvenueno aff
D. Wang, Jennifer Juzwik, Stephen W. Fraedrich, Kurt A. Spokas, Yang Zhang, William C. Koskinen

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsChloropicrinFumigationEnvironmental scienceHorticultureToxicologyAgronomyEnvironmental chemistryChemistryBiology

Abstract

fetched live from OpenAlex

Methylisothiocyanate (MITC) and chloropicrin (CP) are alternatives to methyl bromide for soil fumigation. However, surface transport of MITC emission has been cited as the cause for seedling damage in adjacent fields at several bare-root forest-tree nurseries. Field experiments were conducted at nurseries in Wisconsin and Georgia to measure air emissions of MITC and CP after fumigation. Four treatments were tested as combinations of two fumigants (dazomet or coapplication of CP and metam sodium) and two surface containment treatments (tarp or water seal). A very small percentage (<5%) of the applied equivalent of MITC was lost through either surface treatment over a 2-week period, and lower emissions occurred with the water seal. Cumulative emissions of CP accounted for 10%–22% of the applied CP. With all surface containment treatments, more than 70% of total cumulative emissions of either MITC or CP occurred within 1 week of application. Either surface containment treatment can be used by nursery managers to reduce risk of MITC emissions that could cause damage to nearby seedling crops or harm to humans.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.286
Teacher spread0.256 · 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 designBench or experimental
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

Citations17
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicPlant Disease Management TechniquesFrench-language works237,207