Bibliographic record
Abstract
As part of the intensive field campaign (IFC) for the Boreal forest ecosystem-atmosphere research (BOREAS) project in August 1993, the NASA/JPL AIRSAR covered an area of about 100 km/spl times/100 km near the Prince Albert National Park in Saskatchewan, Canada. At the same time, ground-truth measurements were made in several stands which have been selected as the primary study sites, as well as in some auxiliary sites. This paper focuses on an area including jack pine stands in the Nipawin area near the park. Upon examining the SAR data from stands of old and young jack pine (OJP and YJP) it is observed that the OJP stand produces HH backscatter at P-band which is significantly higher than that from YJP. The OJP trees are taller and have larger diameters, but are much more sparse than the YJP trees. The YJP trees, on the other hand, have denser and greener crown layer. The radar backscatter values for VV polarization and at other frequencies do not manifest such a large difference. For HV backscatter, although P-band return is again larger than C- and L-bands, the difference is not as marked. To explain this distinct signature of OJP stands, a forest scattering model is used in conjunction with the ground-truth measurements. The forest model includes the major scattering mechanisms (volume, double-bounce, and single-bounce) by taking the forest component interactions into account. The contribution from each of the scattering mechanisms to the total backscatter is calculated and their differences for OJP and YJP stands are evaluated. The results are used to discuss the effect of the physical properties of the forest components in each stand on radar backscatter, in particular, for HH polarization at P-band.>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".