Multisource data and knowledge fusion for intelligent SAR sea ice classification
Bibliographic record
Abstract
The authors describe the fusion of various data and knowledge sources for intelligent SAR sea ice classification, thereby addressing the weaknesses of each information source while improving the overall reasoning power of the classifier. They equip their ice classification system, ARKTOS, with the capability of analyzing and classifying images unsupervised by emulating how a human geophysicist or photo-interpreter classifies SAR images. To imitate human visual inspection of raw images, they have designed and implemented a data mining application that first categorizes pixels into regions, and then extracts for each region a complex feature set of more than 30 attributes. In addition, they have incorporated other sea ice data and knowledge products such as ice concentration maps, operational ice charts, and land masks. Finally, they solicited human sea ice expertise as classification rules through interviews, and collaborative refinements during the early-stage evaluations. Using a Dempster-Shafer belief system, they are able to perform multisource data and knowledge fusion in ARKTOS' rule-based classification. ARKTOS has been installed at the National Ice Center and Canadian Ice Service.
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".