TEMPERATURE VARIABILITY AT SENUNU BAY, WEST SUMBAWA
Bibliographic record
Abstract
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. Keywords: temperature, thermocline, variability, ENSO, IODM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".