Data from: Advancing transdisciplinary research on Madagascar's grassy biomes to support resilience in ecosystems and livelihoods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Madagascar-wide metadata relating to Malagasy Grassy Biomes. The understanding of vegetation dynamics in tropical grassy biomes is severely limited across spatio-temporal scales, limiting effective management and support for livelihoods and biodiversity. Despite their extent, utility, and central importance to people and ecosystem function, grassy biomes are often uncritically regarded as degraded, valueless landscapes that result primarily from destructive anthropogenic forces. Moreover, this characterization is often presented without investigation of their history, biodiversity, or ecological complexity. Iconically, Madagascar’s grassy biomes cover approximately 80% of the island’s land surface today and exemplify core challenges to understanding tropical grassy ecosystems and their interactions with anthropogenic activities across spatio-temporal scales. Intersections between human history and environmental change have sparked debates about the role of land use in shaping grassy biomes (e.g., pastoralism, cultivation, fire use), echoing land use debates globally, and highlighting obstacles to ecosystem and livelihood resilience. Like many tropical biodiversity hotspots, Madagascar faces converging challenges that can be aided by an improved understanding of grassy ecosystems and the livelihoods they support, including food and health insecurity, economic inequities, biodiversity loss, climate change, land conversion, and limited resource access. Centered on improved understanding and management of grassy biomes, we present a framework to guide transdisciplinary research across the tropics by: (1) establishing a common terminology; (2) summarising data contributions and knowledge gaps that reflect those in other tropical regions; (3) identifying priority research questions; and (4) highlighting transdisciplinary and inclusive approaches to resolve knowledge gaps and co-benefit ecosystems and livelihoods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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 it