Assessing vertical bioturbation intensity from bedding planes
Bibliographic record
Abstract
Bedding planes hold significant value in ichnological analyses as they improve the recognition of ichnotaxa and yield important insights into the distribution and morphology of trace fossils. However, a significant limitation lies in the challenge of accurately estimating bioturbation intensity from bedding planes. This is in part because current approaches do not establish a connection between bioturbation intensity as seen on bedding planes (plan view) and in cross-section (elevation view). This disconnect complicates the integration of bedding plane assessments into more conventional datasets collected from elevation view. Seventy-seven million Monte Carlo simulations were performed on 11 non-palimpsest Skolithos assemblages, each with varying constraints on burrow length and diameter. These simulations were used to investigate the range and frequency of cross-sectional bioturbation intensities across a spectrum of bedding plane intensities (2–50%). The results show that elevation view bioturbation intensity can be reliably approximated by multiplying plan view bioturbation intensity by the average burrow length relative to the bed thickness, independent of burrow diameter. These findings improve the ichnological analysis of bedding planes for vertical trace fossil assemblages and offer a framework for future research into more complex assemblages.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".