Comparison of Two Harvesting Methods for the Continuous Production of Indigo Plant (Polygonum Tinctorium) Leaves in a Closed-Cultivation System
Bibliographic record
Abstract
Although the blue dye indigo has been chemically synthesized for over a century, there is an increasing interest in the indigo plant (Polygonum tinctorium) as a source of natural dyes and medicines. To maintain a stable supply of P. tinctorium throughout the year, we examined the effect of two harvesting methods on the leaf yield of this plant under a closed-cultivation system. With method 8c-M, all shoot branches >8 cm of the stem bottom were harvested and under method 2b-M, all branches, but two, were harvested at the stem bottom. Both methods enabled sustainable leaf yields from the same plants over 1 year. The total weight of shoot branches harvested by 8c-M was 1.86–3.11 times higher that of shoot branches harvested by 2b-M. Harvesting by 8c-M resulted in shoot branch weights lower than those from plants harvested with the 2b-M. Leaf/shoot ratio was increased in plants harvested by 8c-M. The content of indican, the precursor of indigo, in leaves was not significantly different between the two harvesting methods. Our data may provide a new continuous cultivation method of leaf crops all over years in controlled-cultivation systems.
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 | 0.001 | 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".