Spaceborne Ocean Intelligence Network: SOIN - Fiscal Year 08/09 Year-End Summary
Bibliographic record
Abstract
Abstract : The Spaceborne Ocean Intelligence Network (SOIN) is a six-year research and operational development project that addresses barriers to developing and implementing oceanographic applications derived from Earth-observation sensors such as RADARSAT-2 and MODIS, capabilities that will be provided by the Polar Epsilon Project, combined with existing AVHRR and MERIS sensor data. The project is divided into two phases. The recently terminated three-year Phase I focused on developing state-of-the-art sea-surface temperature and diver-visibility products, operational tools, supporting infrastructure and an ability to detect thermal fronts, eddies and water mass boundaries with RADARSAT-2 synthetic aperture radar (SAR) imagery. The ongoing three-year Phase II will focus on operationalization and implementation of SOIN capabilities. The SOIN project began in June 2007 with funding provided by the Canadian Space Agency through its Government Related Initiatives Program. This report provides a summary of project activities and accomplishments in FY 10/11.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.149 |
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".