An initial analysis of the market potential of a wet ash waste stream from a pulp and paper mill in Newfoundland and Labrador
Bibliographic record
Abstract
The use of biomass ashes generated in forestry operations has been widely studied to decrease costs and the environmental burden associated with ash disposal. The systematic identification, quantification, and characterization of the chemical and physical composition of the ash is an initial and important step identifying feasible end uses. The objective of this study was to outline the methodology to narrow end use options through the use of a case study at an industrial scale: a pulp and paper mill in Newfoundland and Labrador. The potential ash applications were identified through a review of the literature, government regulations, and ash characterization. Three uses in two different sectors were selected based on this assessment: agrarian (as soil amendment) and civil construction (as cementitious material and filler replacement for paving). The case analyzed the market pull factors of the regulatory environment and the push factors of technical feasibility. The results indicate the possibility of using the wet ashes for all areas evaluated (soil amendment, paving, and cementitious materials), with some technical recommendations. Ash as a soil amendment proved to be the most viable option among those analyzed, as the material may be used without further treatment (i.e. reduced costs and environmental impacts) to meet current standards. • Biomass ash application was studied to reduce costs and environmental impact. • Methodology was defined to narrow end use options through a case study. • Key-applications: soil amendment, cementitious material, paving filler. • Wet ashes were considered suitable for all areas with recommendations.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".