An Ichnotaxonomic Approach to Wrinkled Microbially Induced Sedimentary Structures
Bibliographic record
Abstract
Microbially induced sedimentary structures (MISS) are an important facet of recent paleoichnological work because of their taphonomic implications. MISS are extensively studied in terms of their formation processes, recognition in the ancient record, and their diverse morphologies. Classification and terminology schemes are based on their appearance and mode of formation; however, the taxonomic treatment of MISS remains debated. Traditionally MISS have been considered sedimentary structures, and arguments have been made that they cannot be treated as trace fossils under the International Code of Zoological Nomenclature (ICZN) due to MISS being formed by communities of microbiota including algae, cyanobacteria, and others, rather than a single tracemaker. Here, we reexamine MISS using an ichnotaxonomic approach and apply ichnologic terminology and binomial names. Upon reexamining the holotype of Kinneyia Walcott, a genus commonly used to describe some MISS, we argue it cannot be used to correctly describe wrinkle or ripple-like features seen in MISS, and we agree with previous authors that Kinneyia is likely not biogenic in origin. We here assign a new ichnogenus and ichnospecies, Rugalichnus matthewii, to ripple-like sedimentary wrinkle marks known as MISS, separating them from the nomen dubium genus Kinneyia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".