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Record W2088104812 · doi:10.2135/cropsci2004.0137

An Empirical Model for Pollen‐Mediated Gene Flow in Wheat

2005· article· en· W2088104812 on OpenAlexaff
David I. Gustafson, Michael J. Horak, Curtis Rempel, S. G. Metz, D. R. Gigax, Pierre Hucl

Bibliographic record

VenueCrop Science · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of SaskatchewanMonsanto (Canada)
Fundersnot available
KeywordsLogarithmBiologyFunction (biology)Empirical modellingField (mathematics)PollinationBiological systemPollenRegressionRegression analysisStatisticsMathematicsEcologyComputer scienceGeneticsMathematical analysisPure mathematicsSimulation

Abstract

fetched live from OpenAlex

The extent of pollen‐mediated gene flow (PMGF) in wheat (Triticum spp. L.) as a function of distance from a pollinator source has been measured in recent field studies. Wheat is primarily self‐pollinated; however, some cross‐pollination can occur depending on biological, agronomic, and environmental factors. The complexity of these interactions restricts attempts to develop a workable mechanistic model; therefore, we pursued an entirely empirical modeling approach. We fit a simple empirical regression model to all available observed data and then used it to make general predictions about the effects of field size, blending at harvest, and isolation distances on PMGF in wheat. The empirical model was derived by fitting a least squares regression line to the gene flow data when plotted as the logarithm of PMGF versus the square root distance from the edge of the source field. Linear behavior was observed when either the maximum or mean PMGF was plotted in this manner. A “General Wheat Model” (GWM) of this same mathematical form is given which provides a conservative (“high‐end”) prediction of PMGF in the general case: , where PMGF is the percent gene flow at a particular point in the field (without blending), and x is the distance (m) from the edge of the source field. The GWM was used to show that the effect of source field size is minimal for sources of 10 ha or larger, where asymptotic levels of PMGF are obtained. The model was also applied to show that harvest‐blending produces PMGF at the field level 10 to 50 times lower than the highest level observed at the edge of the receptor field. Significantly, isolation buffers of 0 to 10 m were predicted by the GWM to have only a minimal impact on harvest‐blended PMGF, when the receptor field had an overall width of 100 m or greater. Without any isolation buffers, the harvest‐blended PMGF between neighboring commercial‐sized (>10 ha) fields was less than 0.1% (well below commercial thresholds for foreign material in wheat seed and grain). This is also well below any existing standards for labeling the presence of approved biotech traits in food or seed distributed or sold as conventional.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.072
GPT teacher head0.333
Teacher spread0.261 · 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 designSimulation or modeling
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

Citations60
Published2005
Admission routes1
Has abstractyes

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