Bibliographic record
Abstract
Interest in medicinal plants as an alternative crop for greenhouse growers was the impetus for this project, as little information exists on the commercial production of medicinal plants in hydroponics under greenhouse conditions. This research project was designed to (1) identify medicinal plants with commercial potential, (2) evaluate their suitability for greenhouse hydroponic growth, (3) determine optimal concentrations and ratios of nutrient solutions, and (4) manipulate nitrogen levels as a possible regulator of leaf tissue production and of secondary metabolite production in two medicinally important plants, Hypericum perforatum and Tanacetum parthenium . Once nutrient regimes had been optimized to give maximum yield of both target tissues and concentration of target compounds (hence yield per plant of secondary compounds) additional experimental manipulations were carried out to determine if management practices such as leaf removal or flower bud removal could enhance target tissue yields and secondary metabolite production in Hypericum perforatum and Tanacetum parthenium . (Abstract shortened by UMI.)Dept. of Biological Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .S56. Source: Masters Abstracts International, Volume: 41-04, page: 1009. Adviser: Lesley Lovett Doust. Thesis (M.Sc.)--University of Windsor (Canada), 2002.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".