Bedding surfaces: true substrates and Earth's historical archive – an introduction
Bibliographic record
Abstract
Bedding surfaces are innate components of the fabric of sedimentary successions and define the beds that they bound. However, despite their ubiquity across many sedimentary strata and their use and description in a huge variety of geological studies, the importance and diversity of these stratal phenomena can frequently be overlooked and ill-defined. In this introductory text, a brief review of the history of understanding of bedding surfaces is presented: from the seventeenth-century origins of the lithological use of the word ‘bed’ to recent advances such as the isolation and use of true substrates, as bedding surfaces that archive the ancient interface between substrate and air/water. The open questions and contradictions that persist in our understanding of bedding planes, and their utility in interpreting Earth history, are summarized and provide the springboard for introducing the diverse papers in this volume. Together, the papers collected here shed new light on these familiar phenomena from several angles, including sedimentological and stratigraphic discussions of the divergent origins and meanings of different types of siliciclastic and carbonate bedding surfaces, details of practical considerations when using bedding surface signatures in palaeontological and ichnological studies, and a series of case studies illustrating how bedding surfaces (and particularly true substrates) can inform palaeoenvironmental reconstructions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".