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Record W2120112547 · doi:10.1016/j.envpol.2013.10.009

Herbicide impact on non-target plant reproduction: What are the toxicological and ecological implications?

2013· article· en· W2120112547 on OpenAlexaffabout
Céline Boutin, Beate Strandberg, David Carpenter, Solvejg K. Mathiassen, Philippe J. Thomas

Bibliographic record

VenueEnvironmental Pollution · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsCarleton University
FundersMiljøstyrelsenInternationalt Center for Forskning i Økologisk Jordbrug og Fødevaresystemer
KeywordsPhenologyBiologyHabitatEcologyReproductionEcosystemAbundance (ecology)Plant reproductionSeedlingVegetation (pathology)AgronomyPollinationPollen

Abstract

fetched live from OpenAlex

Declining plant diversity and abundance have been widely reported in agro-ecosystems of North America and Europe. Intensive use of herbicides within cropfields and the associated drift in adjacent habitats are partly responsible for this change. The objectives of this work were to quantify the phenological stages of non-target plants in in-situ field situations during herbicide spray and to compare plant susceptibility at different phenological stages. Results demonstrated that a large number of non-target plants had reached reproductive stages during herbicide spray events in woodlots and hedgerows, both in Canada and Denmark where vegetation varies considerably. In addition, delays in flowering and reduced seed production occurred widely on plants sprayed at the seedling stage or at later reproductive periods, with plants sprayed at reproductive stages often exhibiting more sensitivity than those sprayed as seedlings. Ecological risk assessments need to include reproductive endpoints.

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)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations221
Published2013
Admission routes2
Has abstractyes

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