the AAFC Value Chain Round Table Secretariat on behalf of the Ornamental Working Group of the
Bibliographic record
Abstract
The Canadian ornamental horticulture1 industry, with a 2005 farm gate production of approximately $2.2 billion, is one of Canada’s best kept agricultural secrets and success stories (Watson, 2006). However, the domestic market for ornamentals has remained relatively flat, despite the increased interest in gardening and landscaping over the past two decades, because flowers, indoor plants and landscaping have to compete with many other luxury items for the Canadian consumer dollar. The recent slowdown in the Canadian industry has been mirrored around the globe as a result of higher energy and labour costs, increased competition and depressed consumer spending. To survive, the industry has to sell more plants or flowers and obtain higher prices. The four ways to increase ornamental sales are: • Increase the number of purchasing households and younger customers • Increase the frequency of purchases by existing buyers • Increase the transaction value per buying occasion • Create a popular culture of personal use and enjoyment of ornamentals All of these require new and collaborative marketing schemes that promote ornamental flowers and plants in different ways than have been used in the past. The purpose of the project was to provide the Ornamental Working Group of the
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.256 | 0.090 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".