MétaCan
Menu
Back to cohort
Record W4247475043 · doi:10.28930/jitkt.v5i2.7555

TEMPERATURE VARIABILITY AT SENUNU BAY, WEST SUMBAWA

2013· article· en· W4247475043 on OpenAlexaff
Syamsul Hidayat, Mulia Purba, Jorina Waworuntu

Bibliographic record

VenueJurnal Ilmu dan Teknologi Kelautan Tropis · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsThermoclineUpwellingMixed layerBayLa NiñaOceanographySea surface temperatureMonsoonClimatologyGeologyKelvin waveEkman transportEnvironmental scienceAtmospheric sciencesEl Niño Southern Oscillation

Abstract

fetched live from OpenAlex

<p>The purposes of this study were to determine the variability of temperature and its relation to regional processes in the Senunu Bay. The result showed clear vertical stratifications i.e., mixed layer thickness about 39-119 m with isotherm of 27°C, thermocline layer thickness about 83-204 m with isotherm of 14–26°C, and the deeper layer from the thermocline lower limit to the sea bottom with isotherm <13°C. Temperature and the thickness of each layers varied with season in which during the Northwest Monsoon the temperature was warmer and the mixed layer was thicker than those during Southeast Monsoon. During Southeast Monsoon, the thermocline layer rose about 24 m. The 2001, 2006, and 2009 (weak La Nina years), the Indonesia Throughflow (ITF) carried warmer water, deepening thermocline depth and reducing upwelling strength. In 2003 and 2008 thickening of mixed layer occurred in transition season was believed associated with the arrival of Kelvin Wave from the west. In 2002 and 2004 (weak El Nino period,) ITF carries colder water shallowing thermocline depth and enhancing upwelling strength. In 2007 was believed to be related with positive IODM where the sea surface temperature were decreasing due to intensification of southeast wind which induced strong upwelling. The temperature spectral density of mixed layer and thermocline was influenced by annual, semi-annual, intra-annual and inter-annual period fluctuations. The cross-correlation between wind and temperature showed significant value in the annual period.</p><p> </p> <p><strong>Keywords:</strong> temperature, thermocline, variability, ENSO, IODM.</p>

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)0.000
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 score0.025
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.009

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 head0.009
GPT teacher head0.186
Teacher spread0.178 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJurnal Ilmu dan Teknologi Kelautan TropisSame topicGeological and Geophysical StudiesFrench-language works237,207