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Record W2142446131 · doi:10.4141/cjps10125

Simulated hail damage and yield reduction in lentil

2011· article· en· W2142446131 on OpenAlexaffvenueabout
Rosalind Bueckert

Bibliographic record

VenueCanadian Journal of Plant Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCultivarCanopyYield (engineering)CropAgronomyPoint of deliveryReduction (mathematics)Vegetative reproductionBiologyHorticultureEnvironmental scienceMathematicsBotanyMaterials science

Abstract

fetched live from OpenAlex

Bueckert, R. A. 2011. Simulated hail damage and yield reduction in lentil. Can. J. Plant Sci. 91: 117–124. The severity of crop damage by hail is frequently estimated using equations derived from controlled experiments, but this approach has not been extended to the indeterminate pulse crop lentil (Lens culinaris L.). The objective was to simulate hail damage on two lentil cultivars, and estimate yield reduction for use in the Crop Insurance Industry. Hail damage was simulated by controlled canopy crushing on two cultivars, CDC Blaze and CDC Sedley at 4 location-years in Saskatchewan in 2006 and 2007. Plots received simulated damage as the untreated control (0%), 30, 60 or 90% canopy height reduction by crushing at each of four growth stages: vegetative, early flowering, pod-filling, and physiological maturity. As damage intensity increased from 0 to 90%, yield decreased in both cultivars. Most yield reduction (>65%) was seen when damage occurred in reproductive growth. Yield reduction for lentil damaged in vegetative growth was described by linear models, and the reproductive stages by quadratic models. The equations will help improve hail damage assessment in lentil on the Northern Great Plains.

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 score0.823
Threshold uncertainty score0.999

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 head0.045
GPT teacher head0.191
Teacher spread0.145 · 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

Citations23
Published2011
Admission routes3
Has abstractyes

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