Specificity of ENC Data Representation on an Archipelagic Sea Area - Example of the East Coast of the Middle Adriatic Sea Area
Bibliographic record
Abstract
Production of ENCs is based on the theory of multiscale data management (usage bands) and multiple representation of ENC data, controlled by scale minimum (SCAMIN) attributes. This paper presents a solution to the problem of multiscale data management and multiple representation as a part of ENC data production for archipelagic sea areas, using the east coast of the Middle Adriatic Sea area as a typical example. This study is based on a long-standing experience in the production of paper charts and recently ENC production for eastern coast of the Adriatic Sea, which is believed to be the second largest archipelagic area in the Mediterranean. A new method of using SCAMIN attributes for archipelagic seas was proposed, based on Canadian method. Also, a new usage band scale range, compilation scale (twice the chart scale) for all navigational purposes and method of using SCAMIN attributes for archipelagic seas are proposed.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.001 |
| 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".