Comparison of pollen-slide and sieving methods in lacustrine charcoal analyses for local and regional fire history
Bibliographic record
Abstract
The charcoal content from laminated lake sediments in Québec, Canada, was estimated from pollen slides and by a sieving method. The resulting charcoal series are compared to estimate the suitability of these two methods to provide a local or regional fire history. The replication of five different charcoal series from the sieving method shows that this method is suitable for fire-history reconstruction. In our laminated sediments, 1cm 3 is representative of the charcoal content of the sediment. The large charcoal fragments above 15600 mm 2 are too scarce, however, to provide a significant charcoal series. Comparison of the sieving charcoal series versus the pollen-slide charcoal-series shows that the two series display a roughly similar pattern. The differences between the two series probably result from the accumulation of small particles that have a regional source area and are transported by air over long distances and from high fragmentation rates due to laboratory treatment. Spectral analysis for the last 2000 years shows that the sieving charcoal series have no significant periodic accumulation rate, whereas the spectral analysis of the pollen-slide charcoal series shows a significant period of about 500 years. Because the charcoal particles from the sieving method are larger than those from the pollen-slide method, which are potentially windborne over long distances, our study suggests that the sieving method series is a proxy of local fire history, whereas the pollen-slide method is more suitable for detecting regional trends in fire history.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".