Fling it, flail it, squirt it, spray it, spread it, shred it, sleigh it, lay it, application technologies A to Zed
Bibliographic record
Abstract
The term biosolids does little to convey the broad range of material properties associated with its production and use, unless that's the reason they spelled it with a trailing s. Biosolids are routinely applied to land in many forms, as a liquid, thick slurry, semi-solid cake, compost, alkaline amendment, dried pellet, or soil blend - each with distinct handling characteristics. The uses for biosolids products are also wonderfully diverse in objective, fertiliser or soil amendment, and in land form, (e.g. forest, farrrh mine, or park). Designing a product delivery system which accounts for the handling characteristics of the biosolids, the application site characteristics, and the application objectives can be challenging" To meet this challenge, there are a wide variety of technologies and approaches available, which are limited only by your imagination. The trick then is to match the correct technology to the application. In the development of the Greater Vancouver Regional District's Residuals Management Program, we have been faced with many unconventional biosolids application challenges in silviculture, rangeland, and reclamation projects, using a changing variety of biosolids types and products. This has given us cause, or an excuse, to investigate, develop, or test a wide variety of biosolids application methods and technologies. In this paper, we will discuss the results of our trials and tribulations, and suggest which of these technologies are best suited to what types of biosolids applications. It is our hope that some of this information will be helpful to other biosolids recycling practitioners, -and perhaps inspire others to innovate, or perhaps not.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".