Stratigraphic applications of substrate-specific ichnofacies: delineating discontinuities in the rock record
Bibliographic record
Abstract
Abstract Trace fossils represent both sedimentological and palaeontological entities, providing a unique blending of potential environmental indicators in the rock record. Trace fossils and trace fossil suites can be employed effectively to aid in the recognition of various discontinuity types and to assist in their genetic interpretation. Ichnology may be employed to resolve surfaces of stratigraphic significance in two main ways: (1) through the identification of discontinuities using substrate-controlled ichnofacies (the firmground Glossifungites ichnofacies, the hardground Trypanites ichnofacies and the woodground Teredolites ichnofacies); and (2) through careful analysis of trace fossils in vertical (soft-ground) successions (analogous to facies successions). Integrating the data derived from substrate-controlled ichnofacies (so-called omission suites) with palaeoecological data from vertically and laterally juxtaposed softground ichnological successions greatly enhances the recognition and interpretation of a wide variety of stratigraphically significant surfaces. When this is coupled with conventional sedimentary facies analysis and sequence stratigraphy, a powerful approach to the interpretation of the rock record is generated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".