Expression et prédiction du pouvoir tampon des amendements et des engrais organiques
Bibliographic record
Abstract
Masso, C. and Khiari, L. 2013. Computation and prediction of the buffering capacity of organic amendments and fertilisers. Can. J. Soil Sci. 93: 595–606. The buffering capacity (BC) of organic substrates used in agriculture as soil amendments or organic fertilizers are of high interest to prevent adverse effects of mineral fertilizers in the application band near seeds or juvenile seedlings. This is the effect of ammonia, nitrous or aluminum toxicity caused by the sudden and temporary pH flux due to the application of these fertilizers. However, to our knowledge there is no standard method for computing of the BC of organic substrates. In this study, a method by integral calculation, called BCI, was developed and tested on 30 organic amendments and fertilizers (OAF) commonly used in agriculture in Quebec. These OAF were titrated with variable rates of diluted H₂SO₄ or NaOH. A linear and a punctual methods found in the literature were compared to BCI. The integral method was found more appropriate to OAF titration curves, particularly for determining the global BC, integrating the curves obtained during both the acid and alkaline treatments, which was not possible with the other two methods found in the literature. On average, the potential alkalinity of the 30 OAF was higher than the total exchangeable acidity. The globol BCI could be predicted with total Ca, exchangeable Ca, and total Al (r²=0.79). Likewise, the BCI to acidity could be predicted using total Ca, exchangeable Ca, and pHCₐCₗ₂ (r²=0.78).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".