Potential replacements for rockwool as growing substrate for greenhouse tomato
Bibliographic record
Abstract
The greenhouse industry needs renewable, cheap, and available substitutes for rockwool. The physical properties and performance of rockwool substitutes such as low grade peat, composted bark white spruce and fir, shavings, sawdust, and peat-bark mixtures were compared during two greenhouse experiments with tomato grown in plastic bags. Air and water filled porosities greatly differed between substrates, particularly for sawdust and shavings. Relative gas diffusivity (Ds/ Do) and the hydraulic conductivity were less different between substrates. The physical properties of the substrates changed over a production cycle but the changes were small compared to treatment differences. Yields in peat-bark substrates were similar to rockwool substrates during both the short and long experiments but were lower in sawdust and shavings during the long experiment. The yield differences expected between media were less than the differences between some substrate physical properties of the various media. Yields were positively related to easily available water (EAW) and negatively related to Ds/ Do and airfilled porosity (AFP). This indicated excessive drainage for the low-yielding substrates. In plastic bags, media properties related to aeration were not good indicators of production because the plants adapted to the lack of aeration by modifying their root distribution. White spruce and fir bark alone or mixed with low-grade peat showed high potential for greenhouse tomato production and represent an environmental sound alternative to rockwool. Key words: Gas diffusivity, water retention, peat, potting media, sawdust, bark
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".