Mapping global seasonal forest background reflectivity with Multi‐angle Imaging Spectroradiometer data
Bibliographic record
Abstract
Abstract Forest background reflectivities with seasonal and spatial variations are critically important in the estimation of canopy biophysical parameters of the forest canopy. In this paper, seasonal background reflectivity for global forested areas was mapped at 1.1 km resolution using four‐scale model and Multi‐angle Imaging Spectroradiometer data of the nadir and 45° forward directions. The largest seasonal variation of forest background reflectivities was observed in middle and high latitudes of Northern Hemisphere. The background reflectivity differs between deciduous broadleaf forest and coniferous forest in the near‐infrared band and varies with increasing canopy leaf area index. The partial validation of forest background reflectivity with adjacent grassland in the Northern Hemisphere and the comparison of understory leaf area index on leaf appearance day for larch forest in North Asia both indicate the relative reliability of results. The nearly 70% spatial coverage of retrieval with high‐quality flags makes it eligible for applications over global coniferous and deciduous broadleaf forest areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".