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Record W2691810983 · doi:10.22543/0090-0222.2118

Impact of Simulated Insect Defoliation and Timing of Injury on Cabbage Yield in Minnesota

2018· article· en· W2691810983 on OpenAlexaff
E. C. Burkness, Gloria J Gingera, W. D. Hutchison

Bibliographic record

VenueThe Great Lakes Entomologist · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiologyTransplantingSowingYield (engineering)PEST analysisIntegrated pest managementAgronomyCropInsectToxicologyHead (geology)HorticultureBotany

Abstract

fetched live from OpenAlex

In 1992 and 1994, field studies were done to assess the tolerance of transplanted cabbage to simulated insect defoliation and to determine if the defoliation level and growth stage at which defoliation begins influences final yield. In both years, 6 defoliation levels ranging from 0-100% were applied to transplanted cabbage at 4 time intervals. The time intervals began at transplanting, pre-head and head stages and continued until either head stage or harvest. For both years, the only time interval with significantly higher yield than the trans- plant to harvest interval (longest interval) was the head to harvest interval (shortest interval) and significant yield loss occurred only when defoliation was >12.5%. Results suggest that transplanted cabbage can withstand relatively low levels of defoliation before yield loss occurs but that yield loss is also related to the duration over which defoliation occurs. In early growth stages, to protect yield, pest management practices should focus on reducing the interval over which damage occurs. The use of cultural practices that delay the onset of defoliation or allow avoidance of pests could protect yield. These strategies may include using transplants to shorten the time from planting to harvest or using planting dates that allow significant plant growth (i.e., head stage) before defoliators are able to infest the crop and cause significant damage. In addition, management strategies that reduce pest populations can also protect yield but at the head stage should switch to managing pests to protect marketability by reducing aesthetic damage and head contaminants.

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 score0.761
Threshold uncertainty score0.998

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 head0.036
GPT teacher head0.276
Teacher spread0.240 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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