MétaCan
Menu
Back to cohort
Record W2166899464 · doi:10.7202/013076ar

Effect of time of harvest on the incidence of Fusarium spp. in kernels of silage corn

2006· article· en· W2166899464 on OpenAlexafffundvenueabout
Allen Xue, L. Baoluo, Randall M. Clear, B. Vigier

Bibliographic record

VenuePhytoprotection · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsBiologyFusariumSilageFusarium oxysporumFusarium culmorumHybridAgronomyHorticultureVeterinary medicineBotany

Abstract

fetched live from OpenAlex

The effect of time of harvest (at 75, 50 and 25% of milkline) on the incidence of kernel-borne Fusarium spp. was examined in four silage corn ( Zea mays ) hybrids (MAIZEX Leafy 4, NK BRAND Enerfeast 1, PIONEER 37M81 and MYCOGEN TMF94) in Ottawa, Ontario, in 2001 and 2002. Eleven Fusarium species were isolated over the 2 yr. Fusarium subglutinans was the dominant species recovered from 28.8% of the kernels. Other frequently isolated species included F. oxysporum (2.6%), F. graminearum (2.5%), F. proliferatum (0.3%) and F. sporotrichioides (0.2%). Trace amounts (< 0.1%) of the remaining six species, F. avenaceum, F. crookwellense, F. culmorum, F. equiseti and F. solani, were recovered from the kernels. When the kernels were harvested at 75, 50 and 25% of milkline, the incidence of F. subglutinans increased from 20.9 to 26.7 and to 38.7%, respectively; that of F. graminearum increased from 1.7 to 2.9 and to 3.1%; and for the total of the five main Fusarium species it increased from 28.7 to 32.2 and to 42.3%. Incidence of the other species was not affected by harvesting date. Of the four silage corn hybrids, NK BRAND Enerfeast1 had a significantly lower incidence of Fusarium species in kernels than the other hybrids, indicating a genotypic variation in resistance to kernel-borne infection by Fusarium species.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.321

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations8
Published2006
Admission routes4
Has abstractyes

Explore more

Same venuePhytoprotectionSame topicMycotoxins in Agriculture and FoodFrench-language works237,207