Study of Geothermal Characteristics Based on the Geochemistry of Makula Hot Springs Wala Area, South Sangalla, Tana Toraja, South Sulawesi, Indonesia
Bibliographic record
Abstract
Numerous present-day hot springs in the Sulawesi region are divided into two, generally associated with non-volcanic and tectonics geothermal systems, only a small number are associated with active volcanoes, but most do not yet have data to support their utilization.Here we have conducted research at Makula Hot Springs South Sulawesi, the method used is combined geochemical analysis with an estimation of subsurface temperature by using the geothermometer at three sites at Makula Hot Springs.The percentage values of HCO³ˉ, Clˉ, and SO₄²ˉ ion content in hot water samples were analyzed, indicating that the hot springs area was included in the chloride water type.While the results of the estimation of subsurface temperature by using the geothermometer Na -K from the three sites each show the following temperatures: Site I is 124.69℃,Site II is 122.65℃, and Site III is 114.75℃.All sites result in estimations suggested including the low enthalpy which has a temperature limit of <125℃.Furthermore, using the geothermometer Na -K -Mg is known, and the hot springs in the area are included in the partial equilibrium.Geothermal energy in the study area is used for public swimming baths, the development of a tourist attraction, and potentially for a power plant.However, it is still necessary to investigate the geothermal characteristics to maximize the utilization of the hot springs in this area.
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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 | 0.001 | 0.001 |
| 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 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".