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Record W4389391348 · doi:10.1002/ieam.4870

CropLife Europe Crop Development Database: An open-source, pan-European, harmonized crop development database for use in regulatory pesticide exposure modeling and risk assessment

2023· article· en· W4389391348 on OpenAlexaff
Gregory O. Hughes, Andrew Eatherall, Michael I. Bird, Jonathon Blake, P.R. Branford, Sebastian Gebler, A.M. Rodriguez Lozano, P. A. Massey, Gerald Reinken, Adrian S. Terry, Elizabeth Whitworth

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsCollège Montmorency
FundersCropLife Europe
KeywordsDatabaseRisk assessmentEuropean unionCrop protectionComputer scienceRisk analysis (engineering)Environmental scienceBusinessAgroforestry

Abstract

fetched live from OpenAlex

Abstract There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOrum for the Co-ordination of pesticide fate models and their USe (FOCUS) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonized, pan-European, CropLife Europe Crop Development Database (C2D2), that is fully aligned with this regulatory requirement utilizing efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU)1107/2009. Evaluation of C2D2 against an independent data set showed good agreement for equivalent time periods, crop growth stages, and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large data set compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being underrepresented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modeling framework (using the AppDate tool) are often indicated over many growth stages, suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons license to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection. Integr Environ Assess Manag 2024;20:1060–1074. © 2023 CropLife Europe (Corteva Agriscience) and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points A freely available, harmonized, pan-European crop development database (CropLife Europe Crop Development Database [C2D2]) has been developed by CropLife Europe for use in regulatory chemical exposure assessments by collating and harmonizing efficacy trials data generated for regulatory submissions when registering plant protection products. C2D2 comprises a wide range of crops (combinable, root, vegetable, top fruit, soft fruit), spans two decades (2000–2020), and covers 30 countries facilitating comparison with current regulatory scenarios, which suggests that amendment of the existing representation of crop development may be required. Evaluation of C2D2 against an independent data set from Germany for equivalent time periods and crop growth stages showed good agreement, promoting its use in exposure science innovation to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection. C2D2 holds significant opportunities for future development of crop models to improve estimates and timing of chemical exposure, not only for use in chemical fate and transport models but also for nontarget organism, for example, pollinators, effect, and risk assessment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.005
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 head0.042
GPT teacher head0.282
Teacher spread0.240 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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