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Record W3004331393 · doi:10.31851/jipbp.v14i2.3492

PERTUMBUHAN DAN KELANGSUNGAN HIDUP BENIH IKAN NILA (Oreochromis niloticus) DENGAN DOSIS VITAMIN MIX YANG BERBEDA

2019· article· id· W3004331393 on OpenAlexaff
Siti Aidia Mutia, Siswanta Kaban, Sumantriyadi Sumantriyadi

Bibliographic record

VenueJurnal Ilmu-ilmu Perikanan dan Budidaya Perairan · 2019
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBiology

Abstract

fetched live from OpenAlex

Penelitian ini mengenai Pertumbuhan dan Sintasan Benih Ikan Nila dengan Dosis Vitamin Mix Yang Berbeda. Penelitian dilaksanakan bertempat di Kelompok Tani Tambak Mulia, Kota Palembang. Metode penelitian yang digunakan yaitu Rancangan Acak Lengkap (RAL) dengan 4 perlakuan dan 3 ulangan. Hasil penelitian menunjukkan bahwa perlakuan D (penambahan Vitamin mix sebesar 3 % dari jumlah pakan) dipilih sebagai perlakuan terbaik dibandingkan perlakuan lainya. Pada perlakuan D memberikan pertambahan pertumbuhan dan sintasan terbaik yaitu panjang mutlak sebesar 5,77 cm dengan berat sebesar 19,48 gr dan nilai SR sebesar 86,67 %. Untuk perlakuan C pertambahan pertumbuhan panjang mutlak sebesar 5,49 cm dengan berat 17,64 gr dan nilai SR sebesar 80 %. Perlakuan B pertambahan pertumbuhan panjang mutlak sebesar 5,20cm dengan berat 15,35 gr dan nilai SR sebesar 76,67 %. Sedangkan perlakuan A memberikan pertambahan pertumbuhan terendah yaitu panjang mutlak sebesar 5,01 cm dengan berat sebesar 15,03 gr dan nilai SR sebesar 76,67 %. Parameter kualitas air yang diukur selama penelitian masih dalam batas toleransi untuk tumbuh dan berkembang benih Ikan Nila. Dari hasil penelitian yang dilakukan untuk penambahan vitamin mix pada pakan sebaiknya menggunakan dosis 3 %, karena memberikan pertumbuhan dan sintasan yang terbaik. Kata Kunci: Ikan nila, Kandungan Gizi, Vitamin Mix

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, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.015
GPT teacher head0.217
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

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