Bibliographic record
Abstract
A plant biological assay or bioassay for determining compost quality and/or maturity has received attention over the past two decades. However, no universal acceptance for compost quality is evident and cress, which was first reported to be used as a plant bioassay, is still the most commonly used. Furthermore, there is evidence indicating that cress is not sensitive enough to distinguish between mature and immature composts. Fourteen seed propagated species were surveyed to see if one or more would be useful as a bioassay for compost quality. The study confirmed that cress is a less sensitive indicator than several species, for example, lettuce, carrot or Chinese cabbage. Amaranthus tricolor was identified as a potential sensitive indicator species since it did not germinate in an immature compost extract. When the compost extract was diluted, the germination index was linear with extract concentration. While cress responded by differences in root growth, amaranthus responded by reduced germination and root growth which gave it a more definitive response. The study concluded that most of the species, including the commonly used cress, are not sensitive enough to detect differences between mature and immature composts. However, Chinese cabbage appears to be the best of the commonly used assay plants. Amaranthus' potential as a sensitive compost maturity indicator was discovered and more studies are needed to confirm this finding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".