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Record W4386256186 · doi:10.18280/ijsdp.180808

Study of Geothermal Characteristics Based on the Geochemistry of Makula Hot Springs Wala Area, South Sangalla, Tana Toraja, South Sulawesi, Indonesia

2023· article· en· W4386256186 on OpenAlexvenueno aff
Muhammad Fauzi Arifin, Asri Jaya, Ulva Ria Irfan

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeothermal gradientGeologyGeochemistryBlueschistSeismologyEclogiteSubductionTectonicsPaleontology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.218
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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