The legacy of mid‐Holocene fire on a Tasmanian montane landscape
Bibliographic record
Abstract
Abstract Aim To assess the long‐term impacts of landscape fire on a mosaic of pyrophobic and pyrogenic woody montane vegetation. Location South‐west Tasmania, Australia. Methods We undertook a high‐resolution multiproxy palaeoecological analysis of sediments deposited in Lake Osborne (Hartz Mountains National Park, southern Tasmania), employing analyses of pollen, macroscopic and microscopic charcoal, organic and inorganic geochemistry and magnetic susceptibility. Results Sequential fires within the study catchment over the past 6500 years have resulted in the reduction of pyrophobic rain forest taxa and the establishment of pyrogenic Eucalyptus‐dominated vegetation. The vegetation change was accompanied by soil erosion and nutrient losses. The rate of post‐fire recovery of widespread rain forest taxa (Nothofagus cunninghamii and Eucryphia spp.) conforms to ecological models, as does the local extinction of fire‐sensitive rain forest taxa (Nothofagus gunnii and Cupressaceae) following successive fires. Main conclusions The sedimentary analyses indicate that recurrent fires over several centuries caused a catchment‐wide transition from pyrophobic rain forest to pyrophytic eucalypt‐dominated vegetation. The fires within the lake catchment during the 6500‐year long record appear to coincide with high‐frequency El Niño events in the equatorial Pacific Ocean, signalling a potential threat to these highly endemic rain forests if El Niño intensity amplifies as predicted under future climate scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".