Bibliographic record
Abstract
We need automatic pattern recognition algorithms to extract a large statistical sample of granules from high spatial and temporal resolution images series of photospheric fields. In this paper, we present the new Two-level Structure Tracking (TST) algorithm, based on a two-level representation of granulation, which allows us to monitor the characteristics of identified photospheric structures during their duration. TST is also able to retrieve horizontal velocity maps from measured granule displacements. Direct comparison of the results from different works describing granular evolution is often not possible as discrepancies stemming from the use of different procedures or from different data cannot be distinguished. Here, three different solar granulation broadband time series, acquired at THEMIS in July 1999, at NSO-DST in October 1996, and at SVST in June 1995, with different spatial and temporal resolutions, are coherently analyzed via the same TST procedure, allowing direct comparison of the results. Among the obtained results, we confirm the dynamical heterogeneity of photospheric small scale structures pointed out by granular lifetime histograms, characterized by a stretched exponential function. Furthermore, by monitoring the breaking-up of a granule and the movements of its neighbours and of its fragments, we found evidence of how an exploding granule may produce a divergence signal on spatial and temporal mesogranular scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".