Feasibility of Saline Gradient Solar Ponds as Thermal Energy Sources in Saskatchewan, Canada
Bibliographic record
Abstract
Advancement of renewable energy is critical for sustainable development. This paper evaluates the feasibility of saline gradient solar ponds (SGSP) as an alternative energy source for Saskatchewan, Canada. The main achievements include global appraisal of SGSP from theoretical and practical perspectives, assessment of salinity and climatic criteria for SGSP potential, understanding of heat transfer mechanisms affected by thermophysical properties, and numerical modeling to simulate transient heat diffusion in SGSP. Results indicated that Saskatchewan is ideal for thermal energy harvesting from saline water bodies because of high solar insolation (1100 to 1400 kWh/m2). The solar radiation in such systems is captured under a salt concentration gradient. Locally, ten potash tailings sites (360 g/L or 36% salt) and two saline water lakes (250 g/L or 25% salt) are potentially suitable for SGSP deployment. It was found that thermal conductivity increases with temperature but decreases with water salinity increase (0.55 to 0.675 W/mK) and the opposite is true for density (1000 to 1200 kg/m3). Similarly, specific heat capacity slightly increases with temperature and inversely correlates with salinity (3000 to 4200 J/kg K). Furthermore, the heat diffusion model adequately simulated the temperature distribution for a typical SGSP in a potash tailings containment facility. For the investigated month of July (highest solar insolation), the temperatures increased from an initial value of at 20 to 52 oC at top to 37 oC at bottom. A comprehensive risk assessment of this method is required to protect air, water, soil, and biota at specific sites.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".