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Record W2550147261 · doi:10.1094/php-rs-16-0030

Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada from 2012 to 2015

2016· article· en· W2550147261 on OpenAlex

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

VenuePlant Health Progress · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
FundersAgricultural Adaptation CouncilGrain Farmers of Ontario
KeywordsBlightBiologyYield (engineering)AgricultureSan JoaquinAgronomyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Annual decreases in corn yield caused by diseases were estimated by surveying members of the Corn Disease Working Group in 22 corn-producing states in the United States and in Ontario, Canada, from 2012 through 2015. Estimated loss from each disease varied greatly by state and year. In general, foliar diseases such as northern corn leaf blight, gray leaf spot, and Goss's wilt commonly caused the largest estimated yield loss in the northern United States and Ontario during non-drought years. Fusarium stalk rot and plant-parasitic nematodes caused the most estimated loss in the southern-most United States. The estimated mean economic loss due to yield loss by corn diseases in the United States and Ontario from 2012 to 2015 was $76.51 USD per acre. The cost of disease-mitigating strategies is another potential source of profit loss. Results from this survey will provide scientists, breeders, government, and educators with data to help inform and prioritize research, policy, and educational efforts in corn pathology and disease management. Accepted for publication 26 August 2016.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.343

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 head0.014
GPT teacher head0.260
Teacher spread0.246 · 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