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Record W7094217454

ASESMEN KUALITAS AIR PADA TAMBAK PEMBESARAN BENIH UDANG VANAME (Litopenaeus vannamei) BERDASARKAN ANALYTICAL HIERARCHY PROCESS DANWATER QUALITY INDEX

2024· other· W7094217454 on OpenAlexaboutno aff

Bibliographic record

VenueDigilib Repository Unila (Lampung University) · 2024
Typeother
Language
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphogypsumWater quality
DOInot available

Abstract

fetched live from OpenAlex

Provinsi Lampung adalah penghasil udang terbesar di Indonesia. Salah satu kegiatan budidaya udang yang menentukan keberhasilan produksi udang adalah PT. Citra Larva Cemerlang yang menyediakan benih udang yang berkualitas, berlokasi di Kalianda, Lampung Selatan. Penelitian ini bertujuan untuk mengetahui kualitas air pada tambak pembesaran benih udang dengan pengelompokan parameter prioritas penting dalam penentuan kualitas air. Penelitian ini menggunakan metode deskriptif analitik. Data dalam penelitian ini diperoleh dari data pengamatan in situ, hasil analisis di Laboratorium Terpadu dan Sentra Inovasi Teknologi (LTSIT), hasil wawancara, observasi lapangan, dan pengisian kuesioner. Metode analisis data untuk mengetahui kualitas air adalah dengan membandingkan data hasil uji dengan baku mutu air. Dalam mengelompokkan parameter, digunakan Analytical Hierarchy Process (AHP) dan penilaian kualitas air dengan Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI). Hasil yang diperoleh pH (8,03-8,25), suhu (24,24-28,16 οC), salinitas (31,2-33,4%), dan oksigen terlarut (6,6-7,3 mg/L), kekeruhan (0-2,3 NTU), Total Suspended Solid (TSS) berkisar antara 0,33-0,46 mg/L, amonia (0-0,8 mg/L), fosfat (0,01-0,22 mg/L), nitrat (0,16-0,32 mg/L), Biological Oxygen Demand (BOD) berkisar antara 0,77-4,68 mg/L), dan logam berat (Cr, Cd, Cu, Pb, Zn, Ni) yang rata-rata nilainya melebihi baku mutu, hanya kandungan logam Cu tidak melebihi baku mutu air. Analisis parameter kualitas air menggunakan AHP dan CCME WQI menunjukkan bahwa sampel air memiliki kualitas air yang kurang baik. sehingga memerlukan perbaikan kualitas air. Perbaikan dapat dilakukan dengan cara meningkatkan aerasi, penggantian air secara rutin, pengangkatan lumpur, penggunaan probiotik dan pemantauan rutin terhadap parameter kualitas air untuk menjaga keseimbangan ekosistem. Kata kunci: Kualitas air, pembenihan, udang vaname, AHP, WQI

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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