The Iberian Peninsula’s Burning Heart—Long-Term Fire History in the Toledo Mountains (Central Spain)
Bibliographic record
Abstract
Long-term fire ecology can help to better understand the major role played by fire in driving vegetation composition and structure over decadal to millennial timescales, along with climate change and human agency, especially in fire-prone areas such as the Mediterranean basin. Investigating past ecosystem dynamics in response to changing fire activity, climate, and land use, and how these landscape drivers interact in the long-term is needed for efficient nature management, protection, and restoration. The Toledo Mountains of central Spain are a mid-elevation mountain complex with scarce current anthropic intervention located on the westernmost edge of the Mediterranean basin. These features provide a perfect setting to study patterns of late Holocene fire activity and landscape transformation. Here, we have combined macroscopic charcoal analysis with palynological data in three peat sequences (El Perro, Brezoso, and Viñuelas mires) to reconstruct fire regimes during recent millennia and their linkages to changes in vegetation, land use, and climatic conditions. During a first phase (5000–3000 cal. BP) characterized by mixed oak woodlands and low anthropogenic impact, climate exerted an evident influence over fire regimes. Later, the data show two phases of increasing human influence dated at 3000–500 cal. BP and 500 cal. BP–present, which translated into significant changes in fire regimes increasingly driven by human activity. These results contribute to prove how fire regimes have changed along with human societies, being more related to land use and less dependent on climatic cycles.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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 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".