Optimizing exponential growth of Triticum aestivum by application of the relative addition rate (RAR) technique utilizing a computer-controlled nutrient delivery system
Bibliographic record
Abstract
We conducted experiments designed to assess whether plants can be grown under conditions of low ionic strength simulating natural soil conditions. Wheat (Triticum aestivum, cv. Atlas 66) plants were grown using ten different relative nutrient addition rates (RAR) of nutrients (0.00, 0.06, 0.09, 0.12, 0.15, 0.18, 0.21, 0.24, 0.27, and 0.30 day-1) with a background solution containing 50 µmol.L-1 N and all other essential nutrients in optimal proportions relative to N. The relative growth rate (RGR) of plants over a 19-day experimental period responded directly to RAR (from 0.06 to 0.21 day-1) with a linear slope of 0.56 and an intercept of 0.085 day-1. Solution electrical conductivity (EC) values remained relatively stable over the experimental period. We examined the effect of varying nutrient concentrations in background solutions on growth of wheat plants at a RAR of 0.20 day-1. Plant dry mass production in the 50 µmol.L-1 N background solution was higher than the theoretical regression. On the other hand, plant dry mass production in the 0 µmol.L-1 N background solution was close to theoretical regression (considering a relative growth rate of 0.20 g.g-1.day-1) throughout most of the experimental period. Minimal fluctuations in solution EC values were observed over the experimental period. With the use of the computer-controlled nutrient delivery system and the RAR technique, it was possible to grow plants under conditions of low electrical conductivity simulating natural soil conditions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".