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Record W2149137067 · doi:10.21273/hortsci.46.4.610

Investigating Consumer Preference for Organic, Local, or Sustainable Plants

2011· article· en· W2149137067 on OpenAlexaff
Chengyan Yue, Jennifer H. Dennis, Bridget K. Behe, Charles R. Hall, Benjamin L. Campbell, Roberto G. López

Bibliographic record

VenueHortScience · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsVineland Research and Innovation Centre
FundersAmerican Floral EndowmentU.S. Department of Agriculture
KeywordsSustainabilityBusinessOrnamental plantPurchasingGreenhousePreferenceAgricultural scienceMarketingEnvironmental scienceHorticultureEconomicsBiology

Abstract

fetched live from OpenAlex

Organically and locally grown food products have become increasingly popular in recent years. However, unlike food products, consumers purchase most outdoor plants for their aesthetic value rather than their nutritional value. Many of the health concerns related to food products might not be applicable to ornamental plants, so the demand for organic non-food plants is unknown. Using a survey with 834 participants from four states, we investigated consumer preference for ornamentals, vegetable transplants, and herbs grown: 1) organically, locally, and sustainably; 2) in energy-efficient greenhouses; and 3) in biodegradable, compostable, and recyclable containers. Our study found that consumers are not enthusiastic about plants or their fertilizers being “organic.” However, consumers are very interested in plants being produced locally, similar to the public's ever-increasing interest in local food products. Consumers are also interested in purchasing plants in containers that are more sustainable. Among the different types of containers, biodegradable and compostable pots are more desirable than recycled pots.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.215
Teacher spread0.149 · 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 source (direct Gemma or distilled Codex), 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

Citations73
Published2011
Admission routes1
Has abstractyes

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