The role of artificial intelligence in drying and biomass valorization in the field of phytoremediation of contaminated soils
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
The agriculture sector has been acknowledged as the backbone of the economy of many countries. Specifically, phytoremediation can help to increase the GDP of a country by decontaminating the soil that is unavailable for cultivation. Very few research advancements have been reported on soil moisture transport, drying, and biomass valorization. Hence, the present paper highlights the importance of soil moisture transport by highlighting its impact on heavy metal sequestration along with the valorization of biomass. The objective of the present work is to provide a critical overview of the literature about moisture transport, and heavy metals (HMs) leaching in contaminated soils which is unsuitable for agricultural purposes, and simulating its responses using various available artificial intelligence techniques. Furthermore, insights have been made on various approaches to decontaminate soil that can be used for the cultivation of various crops along with other agricultural practices and thereby it can contribute to food safety and security as well as mitigating the global food crisis from waste to wealth conversion.
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 | 0.001 |
| 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 it