Mapping the potential loss and migration of mangroves in Northwest Madagascar due to sea level rise
Bibliographic record
Abstract
Madagascar’s mangroves comprise 2% of the world’s total, but are at risk of loss due to increasing sea level rise (SLR). Vertical elevation gain (VEC) via sediment accumulation has historically allowed mangroves to adjust to SLR, but currently in many parts of the world, SLR is outpacing VEC. This study focuses on the Ambanja-Ambaro Bays (AAB) region in Northwestern Madagascar, with a 7km buffer inland to capture the terrestrial mangrove data and satellite altimeter data for sea level in the Indian Ocean from 2000 to 2016. Rates of change were determined for both SLR based on altimeter data from the Topex/Poseidon and Jason-1, 2, and 3 satellites and VEC data collected from 2016 to 2017 by the NGO Blue Ventures. DEM (from ASTER satellite tiles) and slope were combined with existing land cover classification data from Jones et al. (2016) to determine the potential of mangroves to migrate inland in response to SLR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".