Growth Trials\non Vegetables, Herbs, and Flowers Using\nMealworm Frass, Chicken Manure, and Municipal Compost
Bibliographic record
Abstract
With the growth of the insect farming\nindustry, increasing quantities\nof insect manure (called frass) must be upcycled. This research provides\none of the first sources of information regarding the potential plant\ngrowth enhancement of Tenebrio molitor’s frass on garden plants. It aims at demonstrating that frass\nis a promising fertilizer for plant production. Nine vegetables, one\nherb, and three flowers were planted on the roof of “La Centrale\nAgricole” in Montreal. Plants were grown in a 5% compost-enriched\nsubstrate (v/v) (control) and fertilized with 0.5% (v/v) frass (treatment\n2) or an isonitrogen concentration of hen manure (treatment 3). Plant\ngrowth (germination, height, N flowers) and productivity (biomass)\nwere assessed regularly throughout the growing season. Although beets\nand carrots’ seedling emergence was inhibited by both manures,\nthis did not lead to reduced edible biomass compared to the control\n(germination was unaffected for corn, radish, and arugula). Similar\nto hen manure, frass resulted in a 16-fold increase of the edible\nbiomass as compared to the control. Frass-fertilized plants had larger\nand more numerous flowers than control plants. Our results confirm\nthat insect manure should be recognized as a suitable fertilizer for\nmultiple crops, and should be regulated like other manures.
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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 | 0.000 |
| 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".