MétaCan
Menu
Back to cohort
Record W4415119970 · doi:10.33096/nmnejp40

Studi Analisa Efektifitas Kapal Keruk Pada Proyek Revitalisasi Danau Tempe

2025· article· id· W4415119970 on OpenAlexaff
Andi Butsainah Tumaadir, Sri Mulyani Muchtar, Fatmawaty Rachim, Rudi Hermansyah, Rizki Ayu Saraswati

Bibliographic record

VenueJurnal Teknik Sipil MACCA · 2025
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsTempeDistribution (mathematics)Nanocellulose

Abstract

fetched live from OpenAlex

Danau Tempe adalah salah satu Danau terluas di Provinsi Sulawesi Selatan yang sebagian berada pada kabupaten Sidrap, sebagian berada pada kabupaten Soppeng dan Wajo. Luas Danau Tempe ±13.000 hektar dengan memiliki keliling danau ±79 km. Saat ini kondisi Danau tempe sangat memperihatinkan. Hal ini sesuai dengan data Menteri Negara Lingkungan Hidup Tahun 2011 yang menetapkan Danau Tempe sebagai salah satu diantara 15 (lima belas) danau yang perlu mendapat prioritas penanganan. Seiring dengan pertumbuhan penduduk dan perubahan tata guna lahan yang ada disekitar Danau Tempe, terjadi juga perubahan dilingkungan danau. Berdasarkan data JICA tahun 1980, tingkat sedimentasi di danau Tempe menyatakan bahwa kosentrasi sedimen di danau mencapai 2,4 juta m3 per tahun dan sedimen yang lewat sungai mencapai 1,8 juta m3 per tahun dan diperkirakan tinggi Peningkatan sedimentasi di danau sebesar 1 cm pertahun. Tujuan dari studi ini untuk mengetahui dan menentukan efektifitas kinerja kapal keruk. Dari hasil penelitan dan didaptakan nilai efektifitas dari masing-masing jenis kapal keruk dalam pengerukan danau Tempe yaitu Sand Pump dan Cutter Section Dredger (CSD). Sand Pump 3 (Toyo DPFS-50H) 50 ≥ 35,2619, Sand Pump 4 (Toyo DPF-75BH) 40 ≥26,4833, B-250 Minimax (BP12-10 GG High Chrome) 50 ≥ 17,2915, B-250 Minion (B3025-OV High Chrome) 40 ≥15,4557 dan didapatkan jenis kapal keruk yang paling efektif dalam pengerukan danau tempe yaitu B-250 minion dengan nilai keefektifitasan 40 ≥15,4557.

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 score0.236
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 studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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

Explore more

Same venueJurnal Teknik Sipil MACCASame topicAgriculture and Agroindustry StudiesFrench-language works237,207