Evaluation of information visualization approaches for an enhanced recognized maritime picture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a MISR Visualization Experimental Environment which provides support to the development, evaluation, experimentation, and transitioning of information visualization approaches to emulate the essential elements of a future RMP. The environment provides a means of integrating and sharing the output of visualization tools; storing, accessing and managing showcase examples of visual representations via an underlying visualization reference model; and providing access to underlying data sources supplied through simulation, representative data, and operational data. Visualization design and experimentation activities are also briefly introduced. The MISR experimental environment may be used to characterize the various techniques evaluated and the results of experiments will be introduced as inputs of the environment knowledge base. It is expected that the experimentation undertaken, supported by a MISR experimental environment, will identify novel visualization approaches to be integrated in the future RMP and have the potential to enhance maritime domain awareness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 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 it