Demonstration of nutrient concentration effects on pineapple sage with the aid of automated crop monitoring
Bibliographic record
Abstract
SummaryThe yield and quality of pineapple sage (Salvia elegans Vahl.) grown in two substrates (sawdust and rockwool) and at two nutrient solution concentrations (electrical conductivities of 1.7 and 4 mS cm–1) were compared in each of two years. No consistent effects of substrate were observed on any of the parameters measured, including plant fresh mass, dry mass, leaf area, chlorophyll content or shelf life. Leaf/stem fresh and dry mass ratios were also largely unaffected by substrate. Electrical conductivity (EC), however, had significant effects on most of these parameters. High EC reduced plant size, with lower plant fresh and dry mass, leaf number and leaf area. However, plant quality was improved as demonstrated by increased leaf/stem fresh mass ratio. In addition, chlorophyll concentrations were higher in high EC-treated plants, and shelf life was improved as shown by a reduction in percent water loss in cold storage. Load cells placed beneath the plants were used to monitor and compare transpiration and 24 h growth of high and low EC-treated plants grown in rockwool over a 90 d period. Daily growth recorded between 2400 and 0200 hours was found to be higher in the low EC-treated plants, supporting the observation of greater harvested fresh mass for those plants. Daily transpiration was found to be higher in the low EC plants, due mostly to their greater leaf area compared with high EC plants. When transpiration was compared on a leaf-area basis, no consistent differences were observed between EC treatments. Adapting the principles of a water-balance lysimeter, load cells were also found to be a useful means to check for relative moisture content of the substrate. Since transpiration and water replenishment (supply minus leachate) could be recorded independently, comparison of the two indicates an increase (replenishment > transpiration), decrease (replenishment < transpiration) or no change (replenishment = transpiration) in water content.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".