MétaCan
Menu
Back to cohort
Record W4393003661 · doi:10.35508/aquatik.v6i1.9874

Perbedaan Umur Panen Terhadap Pertumbuhan dan Kandungan Karaginan Rumput Laut Kappaphycus alvarezii Yang Terkena Ice Ice

2023· article· id· W4393003661 on OpenAlexaff
Mega Ida Berepalay, Marcelien Dj Ratoe Oedjoe, Yudiana Jasmanindar

Bibliographic record

VenueJurnal Aquatik · 2023
Typearticle
Languageid
FieldEnvironmental Science
TopicMarine and Coastal Ecosystems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHorticultureBiology

Abstract

fetched live from OpenAlex

Rumput laut tidak memiliki akar, batang, dan daun yang sejati akan tetapi keseluruhan rumput laut disebut talus. Salah satu produk yang dihasilkan dari rumput laut K. alvarezii adalah karaginan. Karaginan merupakan senyawa polisakarida dan dimanfaatkan dalam bidang industri, pangan dan farmasi. Penelitian ini bertujuan untuk mengkaji studi pertumbuhan dan kandungan karaginan dari rumput laut K. alvarezii pada yang terkena penyakit ice-ice. Penelitian ini telah dilaksanakan selama 45 hari mulai dari bulan juli sampai bulan September 2021 di perairan Pasir Panjang, Kota Kupang. Rumput laut dibudidayakan menggunakan metode long line. Pengujian Kandungan Karaginan dilakukan di Laboratorium Fakultas Kelautan dan Perikanan, Universitas Nusa Cendana. Penelitian ini menggunakan Rancangan Acak Lengkap (RAL) dengan empat perlakuan dengan setiap umur panen berbeda yaitu umur panen 15 hari, 25 hari, 35 hari, dan 45 hari. Hasil penelitian kandungan karaginan tertinggi pada umur panen 45 hari 33,3%. Hasil ANOVA menunjukkan perlakuan umur panen berbeda memberikan pengaruh yang signifikan terhadap kandungan karaginan rumput laut K. alvarezii.
 
 Kata kunci : K. alvarezii, Pertumbuhan, Karaginan, Ice- ice

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.008

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJurnal AquatikSame topicMarine and Coastal EcosystemsFrench-language works237,207