Bibliographic record
Abstract
To determine the optimal growing substrate pH values for Sedum plants, Sedum album , Sedum reflexum ‘Blue Spruce’, Sedum spurium ‘Dragon’s Blood’, Sedum hybridum ‘Immergrunchen’, and Sedum sexangulare were grown in containers using peatmoss and perlite-based substrates at five target pH levels (i.e., 4.5, 5.5, 6.5, 7.5, and 8.5). Optimal pH levels, calculated from dry weight regression models, were 6.32, 6.43, 5.71, 6.25, and 5.91 for S. album , S. reflexum , S. spurium , S. hybridum , and S. sexangulare , respectively, and 5.95 overall. Sedum spurium dry weight varied the most among pH treatments (i.e., 9.5 times greater at pH 6.3 vs. 8.3), whereas S. reflexum varied the least (i.e., 1.3 times greater at pH 6.3 vs. 4.4), indicating species-specific growth responses to growing substrate pH. These findings identified a narrow range of optimal growing substrate pH levels within a wider pH range tolerated by five Sedum spp. Therefore, by adjusting substrate pH to optimal levels, Sedum growth can be maximized.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".