Advancing transdisciplinary research on Madagascar's grassy biomes to support resilience in ecosystems and livelihoods
Bibliographic record
Abstract
Abstract Grassy biomes (savanna and grasslands) are globally extensive and host a unique biodiversity that is of central importance to human livelihoods. We focus here on the island of Madagascar—a microcosm of the global tropics, covered in 80% grassy biomes—to illustrate how transdisciplinary approaches to research can clarify ecosystem dynamics, from evolutionary history to human land use. Research on Madagascar's human‐environment interactions has sparked debates about the role of past and current land use in shaping grassy biomes (e.g., pastoralism, cultivation, fire use). These debates echo those in other regions globally, and highlight obstacles to understanding and supporting both ecosystem and livelihood resilience. Like many tropical biodiversity hotspots, Madagascar faces converging challenges that can be aided by transdisciplinary research, including food and health insecurity, economic inequities, biodiversity loss, climate change, land conversion, and limited resource access. We present a framework to guide transdisciplinary research centered on improved understanding and management of grassy biomes on Madagascar by: (1) establishing a globally common terminology; (2) summarizing data contributions and scientific knowledge gaps relating to Madagascar's grassy biomes; (3) identifying priority research questions for Madagascar with applicability in other regions; and (4) highlighting transdisciplinary, inclusive approaches to research that can co‐benefit people and the ecosystems with which they interact.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".