Directly-Georeferenced Hyperspectral Point Cloud (DHPC) from the Mer Bleue Peatland (example dataset)
Bibliographic record
Abstract
Directly-Georeferenced Hyperspectral Point Cloud (HPC_288band_xyz_final.txt) from the Mer Bleue Peatland near Ottawa, Ontario, Canada. The point cloud is accompanied by a meta data file (HPC_288band_xyz_final_META.txt) that records important data parameters such as data acquisition instrument, data acquisition time, data acquisition date, sensor platform, spectral units, wavelength and full width at half maximum of each band, wavelength units, file type and map info. The Directly-Georeferenced Hyperspectral Point Cloud and its advantages over conventional raster data products are described in: Inamdar, D., Kalacska M., Arroyo-Mora J.P., Leblanc G., 2021. The Directly-Georeferenced Hyperspectral Point Cloud (DHPC): Preserving the Integrity of Hyperspectral Imaging Data. Frontiers in Remote Sensing doi: 10.3389/frsen.2021.675323 The methodology to generate the Directly-Georeferenced Hyperspectral Point Cloud is described in: Inamdar, D., Kalacska M., Leblanc G., Arroyo-Mora J.P. 2021. Implementation of the Directly-Georeferenced Hyperspectral Point Cloud. MethodsX Submitted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".