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Record W2605632492 · doi:10.5539/jas.v9n5p33

Coexistence in Oilseed Rape: Effect of Donor Variety Type and Discarding Field Edges

2017· article· en· W2605632492 on OpenAlexvenueno aff
Maren Langhof, Gerhard Rühl

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
FundersBundesministerium für Ernährung und Landwirtschaft
KeywordsOutcrossingPollenBiologyErucic acidCropAgronomyGene flowHorticultureField peaBotanyBrassicaGenetic variationGeneGenetics

Abstract

fetched live from OpenAlex

Oilseed rape is one of the most important sources of vegetable oil worldwide. Approximately 24% of the world’s total cultivation area in 2015 was planted with genetically modified (GM) varieties. Until now the cultivation of GM oilseed rape in the EU is not approved since coexistence of GM and non-GM oilseed rape is a matter of significant public concern. One reason is the differing information about pollen-mediated gene flow in this crop species. Therefore, in a 3-year field trial we investigated gene flow using a GM-free marker system consisting of a high erucic acid oilseed rape (HEAR) variety as pollen donor and a low erucic acid oilseed rape (LEAR) variety as pollen recipient. Donor and receptor fields were equally-sized (75 by 100 m or 0.75 ha) and separated by an isolation distance of 20 m clover-grass. Two different HEAR varieties, a hybrid variety and a line variety, were compared as pollen donor. Generally, outcrossing rates at 1 m field depth were significantly highest. A significant decrease in outcrossing was observed in the first donor-facing 20 m of recipient fields, with no further significant decrease in field depths > 20 m. Outcrossing rates of the total field harvest never exceeded 0.25%. Due to irregular patterns of outcrossing, a separated harvest of the receptor field edge facing the donor plot only marginally reduced the GM content in the total harvest.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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