Application timing optimization of lignite‐derived humic substances for three agricultural plant species and soil fertility
Bibliographic record
Abstract
Coal is mined for energy generation around the world, producing large amounts of waste and extensive disturbances to the environment. Post-mining lands with sandy soils to be reclaimed for agricultural uses are very challenging. The use of humic substances such as soil amendments has been discussed, although little information is available regarding application timing in the field. We conducted a field experiment over two consecutive growing seasons on a former coal mine in China, to investigate soil and vegetation response to a lignite-derived humic product called "nano humus" and to determine optimal application timing. Three economically valuable agricultural species, alfalfa (Medicago sativa L.), barley (Hordeum vulgare L.), and sea buckthorn (Hippophae rhamnoides L.), were used for this study. The benefits of the humic product on soil properties and plant growth under field conditions were expressed after 2 yr of application. A single application at the beginning of each growing season provided better results than splitting into two applications, with no impact of duration (months) between applications. A single application significantly increased soil available phosphorus by 63% and potassium by 96% relative to the control; it significantly enhanced total biomass of alfalfa by 749%, barley by 250%, and sea buckthorn by 147%. Our findings provided important practical implications for using a humic material as a soil amendment in coal mine reclamation, with potential applications in other agricultural and reclamation scenarios.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".