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JENIS-JENIS MAMALIA YANG MENGUNJUNGI KUBANGAN BABI HUTAN DI KAWASAN HUTAN KONSERVASI PT TIDAR KERINCI AGUNG DAN PT KENCANA SAWIT INDONESIA, SOLOK SELATAN, INDONESIA

2017· article· id· W2616984578 on OpenAlexaff
Nurul Insani, Wilson Novarino, Rizaldi Rizaldi

Bibliographic record

VenueMetamorfosa Journal of Biological Sciences · 2017
Typearticle
Languageid
FieldSocial Sciences
TopicAgricultural and Environmental Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsForestryBiologyGeography

Abstract

fetched live from OpenAlex

Penelitian mengenai jenis-jenis mamalia yang mengunjungi kubangan babi hutan di hutan konservasi PT Tidar Kerinci Agung dan PT Kencana Sawit Indonesia, Solok Selatan, Sumatera Barat telah dilaksanakan dari 15 Juni sampai dengan 8 Desember 2015. Penelitian dilakukan dengan pemasangan tujuh buah perangkap kamera di sekitar kubangan babi hutan. Selama penelitian didapatkan 18 jenis hewan mamalia dari 12 famili dan 5 ordo. Hewan mamalia yang sering mengunjungi kubangan babi hutan yaitu Sus scrofa (481 foto), Macaca nemestrina (476 foto), Sus barbatus (269 foto), Macaca fascicularis (38 foto) dan Muntiacus muntjak (33 foto). Penelitian ini menunjukkan bahwa kubangan babi hutan menarik bermacam-macam jenis mamalia dengan frekuensi kunjungan yang berbeda-beda.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.003

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 head0.058
GPT teacher head0.290
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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