Mer Bleue QA4EO Airborne Hyperspectral Imagery
Bibliographic record
Abstract
The data available consist of airborne hyperspectral imagery acquired for the Mer Bleue Arctic Surrogate Simulation Site (MBASSS) S2/L8 Data Product Validation Project in 2016. MBASSS was a collaborative effort aimed at developing a systematic approach for ongoing assessment and validation of satellite based land information products from Landsat 8 OLI and Sentinel 2 satellites. The airborne systems used for this project were the CASI-1500 and SASI-644 hyperspectral instruments (ITRES Research, Calgary AB) installed in the National Research Council Canada Flight Research Lab (NRC-FRL) Twin Otter aircraft. Standard level 2 processed imagery is provided for download as rasters in ENVI Standard format. Imagery is available from April 20, May 11, May 24 and June 23, 2016 as a set of 12 individual flight lines per date. The imagery has been atmospherically corrected and during the geocorrection process, it has been resampled to 1 m pixel size. Currently CASI and SASI imagery are provided separately. Metadata for each flight line is provided in external ascii ENVI header files (*.hdr) and *.met files. The geocorrected imagery provided with pixel level information including pixel view zenith angle (off Nadir angle), DEM, view azimuth angle, radiance path distance, column and row numbers of pixels in non-geocorrected image file, and relative pixel offset between calculated and assigned pixel location. This information is provided in associated *.nad and *.nad.hdr files.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".