High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex
Why this work is in the frame
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Bibliographic record
Abstract
We describe a new high spatial resolution surface water classification dataset generated for the Xingu river, Brazil, from its confluence with the Iriri river to the Pimental dam prior to construction of the Belo Monte hydropower complex, and after its operationalization. This river is well-known for its exceptionally high diversity and endemism in ichthyofauna. Pre-existing datasets generated from moderate resolution satellite imagery (e.g., 30 m) do not adequately capture the extent of the river. Accurate measurements of water extent are important for a range of applications utilizing surface water data, including greenhouse gas emission estimation, land cover change mapping, and habitat loss/change estimates, among others. We generated the new classifications from RapidEye imagery (5 m pixel size) for 2011 and PlanteScope imagery (3 m pixel size) for 2019 using a Geographic Object Based Image Analysis (GEOBIA) approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it