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Record W2041581091 · doi:10.4141/p05-023

Impact of seeding rate and depth on mycosphaerella blight and seed yield of field pea

2006· article· en· W2041581091 on OpenAlexafffundvenueabout
Sheau‐Fang Hwang, R. L. Conner, K. F. Chang, B. D. Gossen, Hui Su, R. J. Howard, G. D. Turnbull

Bibliographic record

VenueCanadian Journal of Plant Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsAgriculture Food and Rural DevelopmentAgriculture and Agri-Food Canada
FundersAlberta Pulse Growers CommissionAlberta Crop Industry Development Fund
KeywordsMycosphaerellaFungicideBlightField peaSeedingAgronomyBiologyChlorothalonilSeedlingYield (engineering)SativumMycosphaerella graminicolaCanopyHorticultureBotany

Abstract

fetched live from OpenAlex

Mycosphaerella blight (Mycosphaerella pinodes) occurs throughout western Canada and can severely reduce field pea (Pisum sativum) seed yield. Field trials were conducted at two sites (Edmonton, AB, and Morden, MB) from 2001 to 2003 to assess the impact of seeding rate and seeding depth on blight severity. Mycosphaerella blight severity in the canopy was greater at higher seeding rates; treatments seeded at 30 plants m -2 had lower levels of disease than those seeded at more than 100 seeds m -2 . However, yield potential was reduced at low seeding densities. Depth of seeding did not affect seedling density, disease severity, yield or final seed weight. In addition, field trials were conducted at Edmonton to quantify yield losses associated with mycosphaerella blight in Alberta. In trials inoculated with M. pinodes, application of a foliar fungicide (chlorothalonil) increased yield by about 20% over the unprotected control. Key words: Pisum, Mycosphaerella, fungicide, crop management, yield loss

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.989

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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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 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

Citations15
Published2006
Admission routes4
Has abstractyes

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