The use of isometric log ratios to classify phosphorus attributes in composts
Bibliographic record
Abstract
This research aimed to classify 16 horticultural composts collected across Canada into management group according to their phosphorus (P) fractions and carbon (C) content using isometric log ratio (ilr) and to interpret the clusters against the total P content and C-to-P ratio indices. The ilr approach was found to be more discriminant for grouping the composts compared with the conventional statistical analysis. The C-to-P ratio index was representative of cluster 1 only. This cluster included organic amendments with C-to-P ratios higher than 100 and high capacity to increase soil organic matter content without excessive P dosage. Total P separated clusters 2 and 3 despite the amalgamation of P forms into total P. Cluster 2 showed high total P (>10 g P kg−1) and low C-to-P ratios (24–38), whereas cluster 3 showed variable C-to-P ratios (18–78) and total P <9 g kg−1 after excluding one compost close to cluster 2. Clusters 2 and 3 were considered as potential sources of plant-available P. The ilr approach suggests that composts made of municipal biosolids and poultry manures in cluster 2 have the highest potential as plant-available P source compared with those made of other livestock manures or food processing wastes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".