A classification for macroscopic charcoal morphologies found in Holocene lacustrine sediments
Bibliographic record
Abstract
Macroscopic charcoal analysis of lake sediment stratigraphies is a widely used approach to reconstruct past biomass burning patterns of ecosystems. The development of fire records often relies on a single quantification method of charcoal in a sediment subsample; however, recent studies have shown that additional paleoecological information can be obtained by classifying charcoal morphologies. The morphologies and diagnostic features of charcoal yields information about fuel sources, fire type, and charcoal taphonomy, and can aid in calibrating sediment records to known historical fires. This additional information enhances paleoecological inferences by providing more paleoenvironmental information than studies of total charcoal as the only metric. Here we present a classification of 27 macroscopic charcoal morphologies observed in Holocene sediments of lakes located in the mixed-conifer forests of southeastern British Columbia, Canada. This classification system builds on other morphological classifications that have been previously utilized, but is more inclusive of the morphological variability observed and is flexible to modification for use when applied to other study settings. The morphological classification presented here was developed following the observation of >100,000 macroscopic charcoal fragments >150 µm. This paper focuses on the observed morphological classes, their identification, potential fuel sources, and the morphotype assemblage stratigraphy from one site as an example. The charcoal assemblages varied throughout the mid-to-late Holocene contemporaneously with known regional scale hydroclimatic changes in British Columbia. Major changes in fire frequency were also concomitant with morphotype assemblage changes. Future work focusing on linking fuel types with charcoal morphotypes, post-fire observations of charcoal taphonomy, and the analysis of multiple attribute charcoal data sets from a variety of ecosystems will improve our understanding of biomass burning and long-term fire ecology.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".