Spent Mushroom Compost as a Substrate for the Production of Lettuce Seedlings
Bibliographic record
Abstract
The successful production of marketable lettuce heads with full exploitation of the genetic potential of a particular cultivar depends on the availability of seedlings of the highest quality. The aims of the present study were (i) to evaluate the effects of substrates containing different proportions of spent mushroom compost on the growth of lettuce seedlings, and (ii) to determine the enduring effects of substrates containing spent mushroom substrate on the vigor of mature lettuce plants derived from such seedlings. The substrates employed in the germination and development of lettuce seedlings were obtained by mixing spent compost from the cultivation of Agaricus subrufescens and commercial vegetable substrate in different proportions. Seedlings were evaluated with respect to the number of leaves and the height, fresh mass and dry mass of the aerial parts, while mature plants were appraised according to stem height and circumference, fresh mass and dry mass of the lettuce head. Substrate containing between 42 to 48% spent mushroom substrate provided the most adequate conditions for the growth and development of crisphead lettuce seedlings and, consequently, of vigorous marketable plants. Under such conditions, the fresh mass of the aerial parts of seedlings attained a level of 0.89 g plant-1, while the fresh mass of the marketable heads achieved a maximum of 233.45 g plant-1. The data presented herein verify that top quality lettuce seedlings yield high quality marketable heads, and that quality improvement can be brought about by the addition of spent mushroom substrate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".