Historical Moisture Content Analysis for Ash Dam Facility in South Africa
Bibliographic record
Abstract
This article articulates a three-year moisture content analysis carried out at the Duvha Ash Dam Facility to identify locations with increasing moisture content values using satellite technology.An increase in moisture content values can be an indicator of failed drainage.For this reason, a historical analysis helps identify locations that have suffered from high moisture content, and this can be validated against historical data held regarding the condition of the ash dam facility.In terms of methodology, the researchers using Soil Moisture Active Passive (SMAP) measured soil moisture.The SMAP mission is an orbiting observatory that measures the amount of water in the surface soil everywhere on Earth.Soil moisture is an important measurement for weather forecasting, failed drainage, drought and flood predictions.The researchers used SMAP radiometers to measure radiation data to calculate water content.The findings were that soil moisture on the ash dam facility stands at 0.09 cm3/cm3.In addition, soil moisture is highest during the summer months at the ash dam facility.During the winter months the soil moisture is low.This makes monitoring of soil moisture generally during the summer months critical.In conclusion SMAP has the advantage of effectively covering large spatial areas at low cost, a regular acquisition of measures over time, and the availability of large historical data archives to perform retrospective studies.However, this technology is yet to be adopted by the South African industry.
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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 | 0.002 | 0.002 |
| 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".