Hydrogel and Organic Amendments to Increase Water Retention in Anthroposols for Land Reclamation
Bibliographic record
Abstract
Using waste materials from industrial activities to build anthroposols (soils built or altered by humans) can provide soil for reclamation and reduce amounts of materials stored in landfills. Mines and other large industrial disturbances requiring anthroposols usually have large amounts of nonorganic waste materials with low water holding capacity and large amounts of coarse fragments. Thus, water holding capacity is a key property to build into anthroposols as all aspects of revegetation are strongly influenced by soil water content. This research assessed the effectiveness of hydrogel and organic amendments to increase the water retention in common mine wastes used to build anthroposols for reclamation in three greenhouse experiments. Waste materials were crushed rock, lakebed sediment, and processed kimberlite, from a northern diamond mine in Canada. Amendments were hydrogel, sewage, salvaged soil, and peat. Pots were filled with the material and weighed and saturated, followed by periodic weighing until the weight was near constant. Water retention was consistently highest in processed kimberlite, with and without amendments. Water retention increased most with hydrogel in processed kimberlite and crushed rock. Hydrogel application method impacted the initial water retention, but over time, the effect was limited. Water retention in lakebed sediment showed little difference relative to no amendment addition and had lowest increases relative to other substrates. Type of waste material and amendment, application rate, and application method impacted water retention and can be adapted to build anthroposols in the field using waste materials suitable for reclamation.
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 | 0.000 | 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) | 0.001 | 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 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".