PySciDON: A python scientific framework for development of ocean network applications
Bibliographic record
Abstract
Remote sensing reflectance is measured by ocean colour satellites, and is used as a proxy for estimation of ocean productivity. However, satellite reflectance data needs to be validated so accurate productivity data is retrieved. To accomplish this, large amounts of in situ above-water reflectance data are collected by the SAS Solar Tracker developed by Satlantic, and installed in moving ships. This provides a large amount of data that needs to be calibrated, flagged for erroneous measurements, and further processed for proper validation of satellite measure reflectance. In this paper we compare our own system, PySciDON, with state-of-the-art commercial software. PySciDON filters data based on longitude, meteorological, and erroneous viewing angle flags, and further calculates reflectance based on input wind speeds, and simulation of different ocean colour satellite bands. As a case study, we tested PySciDON output with data acquired in 2016 on the west coast of Canada with FOCOS (Ferry Ocean Colour Observation Systems) and compared with Prosoft, a proprietary software by Satlantic. The analysis shows that in the early processing stages, PySciDON produces similar data as Prosoft. However, later processing stages show small differences, which could be associated with the interpolation model or some issues we have uncovered with Prosoft.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.021 |
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".