Hydrogel and Organic Amendments to Increase Water Retention in Anthroposols for Land Reclamation
Why this work is in the frame
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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.
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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