MétaCan
Menu
Back to cohort
Record W2974923624 · doi:10.31219/osf.io/pj7ca

Osteologi Ikan Keureling (Tor tambroides)

2019· preprint· id· W2974923624 on OpenAlexaff
Ilham Zulfahmi, Yusrizal Akmal, Muliari Muliari

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBiologyHumanitiesZoologyPhysicsArt

Abstract

fetched live from OpenAlex

ndonesia merupakan negara dengan keanekaragaman ikan yang tinggi, salah satunya ikan yang berasal dari genus Tor. Terdapat 40 spesies ikan Tor di Asia, empat spesies diantaranya hidup di Indonesia yaitu Tor tambroides, Tor douronensis, Tor tambra, dan Tor soro. Saat ini kajian terhadap ikan dari genus ini masih terbatas pada bidang ekologi dan upaya domestikasi. Upaya untuk mengkaji lebih jauh terkait morfologi lebih khususnya osteologi ikan keureling masih belum banyak dilakukan.Kajian morfologi skeleton ikan merupakan bagian penting dalam memahami sistematika ikan, diantaranya untuk mempelajari hubungan taksonomi dan logenetik antar species ikan. Disamping itu, pengetahuan berkenaan dengan deskripsi morfologi skeleton suatu species ikan sangat dibutuhkan sebagai langkah preventif dalam menganalisis abnormalitas sistem skeleton.Buku ini mengulas tentang klasi kasi dan ekologi ikan keureling, metode pembuatan preparat tulang ikan keureling, sistem osteologi ikan, morfologi tulang kepala ikan keureling, morfologi tulang belakang ikan keureling, dan morfologi tulang anggota gerak ikan keureling. Disamping itu, buku ini diharapkan dapat menghadirkan konsistensi dalam hal penyajian nomenklatur tulang penyusun rangka, mengidenti kasi perbedaan osteologi antar spesies ikan, dan memahami hubungan taksonomik dan logenetik antarspesies ikan.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.006

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 head0.040
GPT teacher head0.232
Teacher spread0.192 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicAquatic life and conservationFrench-language works237,207