SRIX4VEG: Surface Reflectance Intercomparison Exercise for Vegetation - NRC, ARSL, NEO
Bibliographic record
Abstract
Over the last ten years, the Applied Remote Sensing Laboratory (ARSL, McGill University) in collaboration with the National Research Council of Canada's Flight Research Laboratory (FRL), has been implementing cutting-edge unmanned aerial vehicle (UAV) hyperspectral systems for diverse applications (e.g., biodiversity, subaquatic vegetation, wildfires), with a significant focus on satellite product validation methodologies for northern ecosystems – e.g. peatlands. SRIX4Veg is endorsed by CEOS (Committee on Earth Observation Satellites) and is funded by the European Space Agency (ESA). The ARSL, FRL and Norsk Elektro Optikk HySpex teamed up to participate in the SRIX4VEG: Surface Reflectance Intercomparison Exercise for Vegetation experiment, at the Las Tiesas Experimental Station, Barrax, Spain. This intercomparison experiment included teams from Europe, US and Canada, aiming to develop a protocol for best practices for UAV hyperspectral image acquisition for satellite land product validation within ESA’s Fiducial Reference Measurements for Vegetation (FRM4VEG) project. Here we summarize the imagery acquired by our team during the field campaign at the July 18-22, 2022 field campaign. Meta data includes a description of the imagery acquired under the user-defined methodology. A future update will include metadata for the imagery acquired under the common experiment parameters defined by the organizers.
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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".