MétaCan
Menu
Back to cohort
Record W4387265776 · doi:10.1094/phytofr-08-23-0109-a

Field Crop Yield Loss Calculator for Disease and Invertebrate Pests: An Online Tool from the Crop Protection Network

2023· article· en· W4387265776 on OpenAlex
Daren S. Mueller, Adam Sisson, Brittany Eide, Thomas Wesley Allen, Carl A. Bradley, Travis Faske, Andrew Friskop, Kathy Lawrence, Fred R. Musser, Dominic Reisig, Albert Tenuta, Kiersten Wise

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePhytoFrontiers™ · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsMinistry of Agriculture, Food and Rural Affairs
FundersOntario Ministry of Agriculture, Food and Rural AffairsNational Corn Growers AssociationUnited Soybean BoardCotton Incorporated
KeywordsCalculatorCropYield (engineering)AgricultureCrop protectionCommodityGovernment (linguistics)Resource (disambiguation)Agricultural engineeringAgricultural economicsAgroforestryBusinessComputer scienceBiologyAgronomyEngineeringEcologyEconomics

Abstract

fetched live from OpenAlex

The Field Crop Disease and Invertebrate Loss Calculator Web Tool is an interactive online resource showcasing estimates of the yield reduction and economic impact of plant diseases and invertebrate pests over time and location. Accessible through the Crop Protection Network website ( https://www.cropprotectionnetwork.org ), this free online research tool was developed to inform agronomists and researchers across industry and academia, extension workers, commodity groups, and funding agencies about the impact of crop disease and invertebrate pests. The tool enables widespread user access through commonly used web browsers on computers, tablets, and mobile devices. Yield loss estimates are generally obtained through annual surveys of university-affiliated specialists or others with experience in crop protection. Production data are primarily obtained from U.S. and Canadian government agencies. A detailed understanding of the economic impacts of plant disease and invertebrate pests provides valuable information that can inform the allocation of resources to reduce losses in major field crops. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.564
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.052
GPT teacher head0.238
Teacher spread0.186 · 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