Utility of wood ash, paper sludge and biochar amendments for the \nmitigation of greenhouse gas emissions from acid boreal soils
Bibliographic record
Abstract
Newfoundland and Labrador (NL) soils are characterized with low fertility, shallow, and acidic \nsoils which impede agricultural productivity. Developing agriculture is a goal for the provincial \ngovernment of NL and to promote soil fertility and crop productivity, manure and inorganic \nfertilizers application are required which may lead to higher greenhouse gas emissions (GHGE) \nfrom agricultural fields or cropping systems. Efforts to reduce reliance on synthetic soil \namendments while taking advantage of abundant locally sourced industrial waste by-products such \nwood ash and paper sludge available in the province must be examined. Therefore, I hypothesized \nthat wood ash, paper sludge and biochar may be a good source of soil amendments for improving \nsoil health or fertility of boreal soils. I assessed the microbial activities leading to nitrogen losses \nand availability and functional state of the bacterial and archaeal genes (napA, narG, nirS, nirK, \nand nosZ) driving nitrogen transport as putative proxy indicators to produce CO2 and NOx \nemissions. This study provided insights into the recommendations on the suitability and \npotential utility of wood ash and paper sludge for improving soil quality, health, and thus \nagricultural productivity.
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 | 0.000 | 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 | 0.001 | 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".