Connecting Users with Their Data: An Environment to Explore the Morphodynamics of Rip Channels
Bibliographic record
Abstract
Remote sensors are widely used in coastal morphodynamic research, and users are often confronted with abundant data sensors generated by these sensors. These data, if properly explored, can be used to study and manage many coastal features and processes that are not well understood. As an example, we present the exploration of rip channels. Coastal data sets are currently explored using animated image sequences. Users are dissatisfied, however, because exploration remains largely a subjective and time-consuming process. In particular, detecting the presence and evolution of relatively small, highly dynamic rip channels has proved difficult. This article examines how the visual exploration of rip objects can be improved. We first look at the factors limiting exploratory use of conventional animations for rip studies and argue that two main factors are responsible: data complexity and animation design based on images that mimic reality. Then we present an example of how the current approach to visualizing time series of coastal images can be improved by computational methods, particularly by feature tracking. Next, we describe a visualization prototype and discuss the representational, data-mining, and interactive functionality resulting from such a combination in an environment dedicated to the exploration of dynamic objects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".