African fire histories and fire ecologies
Bibliographic record
Abstract
Patterns of fire are changing across African savannahs, rainforests, fynbos, woodlands, and Afroalpine and montane forests, with direct environmental and socio-ecological consequences. Fire variability has implications for biodiversity (Beale et al. 2018), vegetation patterns, grazing quality, carbon emissions, protected area management, and landscape heterogeneity. Fire is a crucial component of savannah functioning and structure and is essential for maintaining its biodiversity. Long-term records are key to understanding drivers of fire variability and contextualize recent and ongoing land-use changes that altered fire responses to climate and vegetation changes (e.g. Ekblom and Gillson 2010, Colombaroli et al. 2014). As indigenous forest loss continues and modification through selective harvesting and land-use encroachment accelerate forest changes, the importance of historical disturbance regimes is increasingly relevant for assessing past ranges of variability and to define management targets that support more resilient socioecological systems (Whitlock et al. 2018). But how can the research community engage and integrate with land-management practitioners and policy developers? And how can we promote knowledge transfer and collaborative capacity between the international community and the next generation of African scientists?
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.
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 | 0.000 | 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 | 0.000 |
| 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 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".