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Record W2536368203 · doi:10.21273/hortsci.41.3.509b

ORGANIC VEGETABLE CULTURE IN MISSISSIPPI: GROWING AND PROFITABLE

2006· article· en· W2536368203 on OpenAlexaboutno aff
William B. Evans, Kenneth Hood, P.M. Hudson, Keri L. Paridon

Bibliographic record

VenueHortScience · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCucurbita pepoSquashCultivarYield (engineering)CropProduction (economics)AgronomyCucumisAgricultural scienceBiologyEnvironmental scienceHorticultureEconomics

Abstract

fetched live from OpenAlex

Yield and economics of vegetable crops are being evaluated in non-adjacent organic (OG) and nonorganic (NOG) vegetable production field areas in Crystal Springs, Mississippi. Each production area has six sections in which crops are rotated over several seasons and years. Production techniques and management are as similar in timing and methodology as possible between the systems without compromising either system. Production methods, timing, and costs are recorded for each operation. These are combined with yield data to create budgets and estimated returns for each production system/crop combination. When possible, harvested produce is marketed by a cooperating grower-retailer at a local mid- to up-scale farmers market. Three years into the study, positive returns have been found for several crops including potato ( Solanum tuberosum L.), lettuce ( Latuca sativa L.), summer squash ( Cucurbita pepo L.), cucumber ( Cucumis sativa L.), and others. Marketable new potato yields in 2005 were under 10,000 lb/acre for Yukon Gold and Red Lasoda in either production system. Estimated net returns, based on an actual $2.00/lb market price, were positive for all system/cultivar combinations although final budget numbers are not firm. Significant differences in yield among cultivars were seen in potato, lettuce, summer squash, and cucumber. Organic production budgets for other crops in the study are also being developed.

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 score0.874
Threshold uncertainty score0.328

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 communication0.0000.001
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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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