Maintaining exponential growth, solution conductivity, and solution pH in low-ionic-strength solution culture using a computer-controlled nutrient delivery system
Bibliographic record
Abstract
Studies of plant nutrient requirements in solution culture have often used nutrient concentrations many-fold higher than levels found in fertile soils, creating an artificial rooting environment that can alter patterns of nutrient acquisition. The relative addition rate (RAR) technique addresses this problem by providing nutrients in exponentially increasing quantities to plant roots in solution culture. A computer-controlled RAR nutrient delivery system has been developed to reduce workload and to facilitate more frequent nutrient additions (4x daily) than is possible with manual additions. In initial experiments, a minimum background solution containing 500 microM nitrogen and all other essential nutrients in optimal proportions was required for the healthy growth of Triticum aestivum. This requirement was reduced to 50 microM nitrogen when calcium in the background solutions was increased to 400 microM. Varying the abundance of ammonium and nitrate in both background and delivery solutions provided a means of controlling plant-induced pH changes in growth solutions. In optimized solutions, plant relative growth rates (RGR) in the order of 0.2 g g(-1) plant d(-1) were maintained over a 22 d experimental period. Variation in RARs provided a means of growing plants with varying RGRs under relatively constant conditions of solution electrical conductivity and pH.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".