Biogenic potassium: sources, method of recovery, and sustainability assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Nutrient management methods based on ecosystems are crucial for providing agricultural nutrient needs while reducing the environmental impact of fertilizer usage. With increasing agricultural production, the global demand for potassium is increasing, with India importing potassium from countries like Canada, USA, Israel, and Russia. Biomass-fired industries generate biomass ash as a residue so management of the resultant ash is important. Agricultural residue ashes contain abundant potassium so could potentially be used for fertilizer application. This review describes different potassium sources and recovery processes, including chemical precipitation, water extraction, solvent extraction, membrane separation, and ionic exchange. Extraction time, temperature, and solid to solvent ratio affect the recovery of potassium from biomass ash. Water extraction is the most commonly used method for potassium recovery from biomass ash. The environmental impact of potassium fertilizer recovered from biomass ash is less than that of mining source of potash. This paper discusses topics not covered in previous reviews, such as different biosources of potassium, latest recovery methods, and life cycle assessment of these methods. The gaps identified in the reports are addressed, and future research opportunities are presented.
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.
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 | 0.000 |
| 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 it