Evaluation of Compost Leachates for Plant Growth in Hydroponic Culture
Bibliographic record
Abstract
Compost run-off leachates are usually rich in nutrients and can potentially be recycled in plant culture. Seedlings of tomato (Lycopersicon esculentum Mill. ‘Roma VF’) and marigold (Tagetes erecta L. ‘Crackerjack’) were grown for five weeks in each of six hydroponic treatment solutions: full and half strength Hoagland's solution; leachates from spent mushroom compost (SMCL) and pond-collected runoff from a commercial composting operation (RCL); and these leachates amended with extra nitrogen (N) and phosphorus (P) (SMCL+NP and RCL+NP, respectively). The leachates were low in content of N and P, but rich in potassium (K), magnesium (Mg), calcium (Ca), sodium (Na), and various microelements. Top dry weight of tomato seedlings was highest in the two Hoagland's solutions, intermediate in RCL+NP, and lowest in the other solutions. With marigold, top dry weight in SMCL+NP was similar to that in the two Hoagland's solutions, intermediate in SMCL, and lowest in the RCL+NP and RCL solutions. The poor tomato performance on the SMCL+NP regime was the result of a high proportion of NH4-N ( < 50%), which over time lowered pH and caused nutritional disorders. Imbalance in tissue nutrient (low content of N and P and high content of K, Mg, Ca, and Na) from plants grown in the unamended SMCL and RCL solutions indicates a need to balance these nutrients in the leachates before it is recycled.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".