Variability and repeatability of quantitative, SD-OCT doppler blood flow in young and elderly healthy subjects
Bibliographic record
Abstract
Good knowledge of the formation and recognition of the bright band is necessary to determine the location of the melting layer. This is partly because the melting layer is one of the major hydrometeors (others include as rain, hail, and cloud) responsible for signal degradations along the slant-path, in the tropical regions of the world. These may result in signal fading, amongst others, which may lead to errors in slant-path attenuation predictions. This paper involves the comparative analysis of radar data sourced from both ground 3D RAPIC bistatic radar and space-borne precipitation radar above the Malaysian air space. For this research work, the terrestrial meteorological radar data were sourced from the Meteorological Department of Malaysia, while the satellite radar data were obtained from the near-real-time TRMM Multi-Satellite Precipitation Analysis (TMPA-RT) version 7 products. Frozen hydrometeors are observed to exhibit peculiar characteristics in terms of increased radar reflectivity as they fall from the sky, transiting from solid to liquid, and manifesting in the popular bright band signature. The melting layer is the region where melting occur, just below the 0? isotherm height. It is a major factor responsible for the problems being encountered in characterization and modelling of microwave signal propagation along the earth-space link. \n, Nor Hisham Khamis
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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 | 0.002 | 0.004 |
| 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) | 0.000 | 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".