Competitive analysis of current Ocean Web-Mapping Applications
Bibliographic record
Abstract
A competitive analysis study is a usability engineering method administered to critically analyze and compare a set of similar applications according to their relative merits. This paper presents a competitive analysis study of current ocean web-mapping applications that deliver ocean related data to the scientific community. The analysis is part of a User-Centered Design (UCD) approach that was applied to develop the Ocean Web-Mapping Application of the Ocean Mapping Group (OMG) at the University of New Brunswick (UNB), a web mapping application to deliver ocean mapping data to ocean modellers. A total of twenty-four existing applications were critically analyzed and compared across two broad themes in cartography: (1) representation and (2) interaction; adding topics to consider the potential needs of ocean modellers. The results helped to establish trends and gaps and to discover new opportunities for ocean web-mapping development. Using the conclusions drawn from this study, an online survey was prepared to be conducted by ocean modellers and continue the UCD methodology of the Ocean Web-Mapping Application.
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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 | 0.097 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".