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Record W4399691647 · doi:10.30996/ep.v21i01.9086

Analisa Sebaran Oksigen Terlarut Dan Korelasinya Dengan Suhu Permukaan Laut Menggunakan Citra Landsat-8 (Studi Kasus: Wilayah Pesisir Kota Tuban)

2024· article· id· W4399691647 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEXTRAPOLASI · 2024
Typearticle
Languageid
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Daerah yang termasuk kedalam bidang kelautan dan kelautan adalah daerah Tuban. Daerah Tuban sendiri memiliki 6,5 km garis pantai dan 22.068 km² luas laut. Misalnya, di daerah Kutorejo di pesisir Tuban, banyak warga sekitar yang memandang pantai sebagai sumber kehidupan utama mereka dalam hal pariwisata, perikanan, dan industri lainnya.Teknologi yang digunakan yakni pengindraan jarak jauh.. Instrumen satelit juga dapat digunakan dalam penginderaan jauh. Penelitian ini bertujuan untuk menganalisa sebaran suhu permukaan laut dan konsentrasi oksigen terlarut di wilayah pesisir Kabupaten Tuban. Hasil analisa menunjukkan bahwa nilai data insitu untuk suhu permukaan laut sebesar 30,50° - 33,70° C dan untuk oksigen terlarut sebesar 1,26 – 6,20 mg/L. Sedangkan untuk data citra dengan korelasi tertinggi untuk suhu permukaan laut menunjukkan bahwa nilai untuk data citranya sebesar 31,00° – 31,11° C dan untuk oksigen terlarut sebesar 10,86 – 11,49 mg/L. Kedua data insitu tersebut memiliki korelasi positif yang cukup rendah.

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.

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, Insufficient 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 score0.331
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.005

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 head0.225
Teacher spread0.207 · 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