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Record W4410571537 · doi:10.36733/jikt.v14i1.10984

Analisa Kualitas Air pada Daerah Terintrusi Air Laut di Pantai Lovina

2025· article· id· W4410571537 on OpenAlexaff
Ketut Agus Karmadi, Putu Edi Yastika, Luh Natasya Karini Putri Karmadi

Bibliographic record

VenueJurnal Ilmiah Kurva Teknik · 2025
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsEnvironmental science

Abstract

fetched live from OpenAlex

Intrusi air laut merupakan fenomena masuknya air asin ke dalam airtanah (tawar). Salah satu kawasan yang terdampak oleh peristiwa tersebut adalah Pantai Lovina. Hal ini disebabkan oleh banyaknya pembangunan dan pengembangan fasilitas pariwisata sehingga terjadi pengambilan airtanah yang berlebihan. Tujuan penelitian ini adalah untuk mengetahui kualitas air pada daerah yang terdampak intrusi air laut di Pantai Lovina. Metode yang digunakan adalah dengan mengambil contoh air dengan dilakukan pengujian terhadap parameter TDS (Total Dissolved Solid), EC (Electric Conductivity), dan pH (Power of Hydrogen). Setelah dilakukan uji terhadap parameter-parameter tersebut maka didapatkan hasil 2.Kualitas air tanah pada zona intrusi air laut di sepanjang Pantai Lovina berdasarkan kadar TDS pada daerah penelitian paling rendah sekitar 503 ppm hingga 5074 ppm di mana pada sumur yang memiliki TDS lebih dari 1000 ppm maka daerah tersebut terindikasi intrusi air laut di mana terdapat 7 (tujuh) sumur dari 14 sumur yang terintrusi berdasarka hasil survei pada daerah penelitian. Lalu untuk kadar EC (Electric Conductivity) rata-rata memiliki kadar lebih dari 1000 mS/cm yang di mana pada daerah timur daerah penelitian memiliki kadar 1006 – 1741 mS/cm yang menandakan kualitas air buruk dan adanya potensi intrusi air laut, lalu pada daerah barat penelitian memiliki kadar 2022 – 4206 mS/cm yang menandakan intrusi air laut. Maka dapat disimpulkan bahwa daerah penelitian telah mengalami intrusi air laut.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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.

Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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