Mechanical properties of <i>Polygonatum multiflorum</i> leaves after treatment with growth stimulants
Bibliographic record
Abstract
The mechanical properties of leaves are important in many aspects of plant science. Because of their delicate structure, leaves are sensitive to different, potentially harmful, environmental factors. The mechanical properties of leaves are important factors affecting leaf quality, longevity, susceptibility to damage, and decomposition. Two growth stimulants were applied to investigate selected mechanical properties of Solomon’s seal Variegatum [Polygonatum multiflorum (L.) All.] leaves grown under field cultivation and in an unheated polytunnel. The mechanical properties of leaves were assessed by measuring Young’s modulus. The agents used in the treatment were Actisil Hydro Plus at a concentration of 0.4% in the first series and Pentakeep V at a concentration of 0.04% in the second series. Foliar treatment with the stimulants was conducted six times at weekly intervals. The control plots were sprayed with distilled water. The research was carried out between 2012 and 2014. In comparison to the test series, the respective effects of Actisil Hydro Plus and Pentakeep V on the change in Young’s modulus was more notable in plant leaves obtained from the tunnel than those from field cultivation. Specifically, growth stimulants had more impact on the stiffness of leaves obtained from plants grown in the tunnel. Generally, plant leaves from field cultivation were stiffer than those from the tunnel.
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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 | 0.001 |
| 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".