An 11,000 year fire history of a coastal temperate rainforest in Prince Rupert Harbour, British Columbia
Bibliographic record
Abstract
This thesis utilizes high resolution charcoal analysis and charcoal morphology to reconstruct fire history for Prince Rupert Harbour (PRH), British Columbia between ~13,200 and 3500 cal BP. Home to the North Coast Tsimshian, PRH is an ideal study location for evaluating demographic and related environmental patterns for its extensive and well-studied archaeological record yet it lacks local paleoenvironmental data. I use this fire history to: produce one line of localized palaeoecological data, assess variation in charcoal accumulation rates (CHAR) compared against regional and semi-local paleoclimate records, determine whether or not the presence of an anthropogenic signal exists in the charcoal record, and test the relationship between natural charcoal accumulation rates and demographic models for the PRH region between 6000 and 3500 cal BP. Results indicate that fire frequency and intensity were low with just fourteen potential fire events in the DIL CHAR record, 9 in Zone 2 (13,200-6000 cal BP) and 4 in Zone 1 (6000-3500 cal BP). Zone 2 peaks could not be confidently separated from the background signal and are interpreted with caution. Peaks charcoal morphology in Zone 1 in suggest local, low intensity fires. The data suggests that no extreme fluctuation in climate as seen through fire regime occurred over the 11,000-year record, and while there is no correlation between demography and CHAR such that an anthropogenic driver could be posited, the only fire events confidently identified occurred after the earliest known villages formed. I suggest that additional fire histories capturing the past 3500 years when PRH saw significant demographic growth will be necessary and fruitful for understanding the human impact, if any, on the fire regime of the Prince Rupert Harbour region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".