Combination of target scattering decomposition with the optimum degree of polarization for improved classification of boreal peatlands in the Athabasca region
Bibliographic record
Abstract
Target scattered wave polarization signature is introduced for the representation of the variations of the main scattered wave parameters as a function of the transmitting antenna polarization. It is shown that the signature of the degree of polarization (DoP) and the total scattered intensity (R0) provide important information that is complementary to the Van Zyl conventional received intensity polarization signatures. As a result, the DoP optimization is used as an additional source of information in complement with target scattering decomposition for optimum characterization of peatlands and their surrounding upland forests. The study is conducted using polarimetric L-band PALSAR data collected over boreal peatlands in the Athabasca oil sand exploration region (near Fort McMurray, Alberta). The potential of polarimetric L-band PALSAR and the Touzi decomposition for monitoring water flow beneath the peat surface is confirmed. The scattering type phase permits an enhanced discrimination of poor fen from bogs; two wetland classes that can hardly be discriminated by optic and conventional SAR sensors. The complementary information provided by the DoP optimization permits better discrimination of burned from healthy forests. The DoP dynamic range as well as the scattering phase, which is sensitive to peatland subsurface water flow, permit the right assessment of peat health in burned black-spruce bogs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".