Study of Arabian Seawater Temperature Fluctuations
Bibliographic record
Abstract
It is well known that the ocean has an important role in climate variability and change. To study the variations in sea-surface temperature (SST) of Arabian sea near Karachi coastal region, we apply the probability distributions theory as it gives more insights of SST fluctuating behavior. In this regard the adequacy of Normal. Gamma, and Lognormal probability distributions is tested with the help of Kolmogorov-Smirnov D-test. It is found that most of the months of the year follow Normal probability distribution, whereas April, August, October, and November follow Lognormal probability distribution. Further, using the distribution parameters mean and standard deviations of monthly SST are also calculated, which come out to be (23.33 ± 0.316), (23.19 ± 0.300), (24.36 ± 0.312), (26.27 ± 0.360), (28.31 ± 0.325), (29.19 ± 0.347), (28.64 ± 0.346), (27.38 ± 0.364), (27.34 ± 0.322), (27.61 ± 0.311), (26.43 ± 0.352), (24.65 ±0.380).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 teacher head, 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".