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Record W4408016810 · doi:10.1126/science.adm7634

Too much of a good thing: Lessons from compromised rootworm Bt maize in the US Corn Belt

2025· article· en· W4408016810 on OpenAlexaff
Ziwei Ye, Christina DiFonzo, David A. Hennessy, Jinhua Zhao, Felicia Wu, Shawn P. Conley, Aaron J. Gassmann, Erin W. Hodgson, Bryan Jensen, Janet J. Knodel, Bradley McManus, Lance J. Meinke, Andrew P. Michel, Bruce Potter, Nicholas J. Seiter, Jocelyn L Smith, J. L. Spencer, Kelley J. Tilmon, Robert Wright, Christian H. Krupke

Bibliographic record

VenueScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBacillus thuringiensisGenetically modified maizeWestern corn rootwormAgronomyBiologyCropSowingPEST analysisTragedy of the commonsGenetically modified organismAgricultureGenetically modified cropsAgroforestryZea maysEcologyCommonsBacteriaTransgeneBotany

Abstract

fetched live from OpenAlex

Widespread use of genetically engineered maize targeting the corn rootworm complex ( Diabrotica species) has raised concerns about insect resistance. Twelve years of university field trial and farm survey data from 10 US Corn Belt states indicate that maize hybrids expressing toxins derived from the bacterium Bacillus thuringiensis (Bt maize) exhibited declining protection from rootworm feeding with increased planting while pest pressures simultaneously decreased. The analysis revealed a tendency to overplant Bt maize, leading to substantial economic losses; this was particularly striking in eastern Corn Belt states. Our findings highlight the need to go beyond the “tragedy of the commons” perspective to protect sustainable use of Bt and other crop biotechnology resources. We propose moving toward a more diversified and transparent seed supply.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.010
GPT teacher head0.290
Teacher spread0.280 · 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

Citations19
Published2025
Admission routes1
Has abstractyes

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