Bibliographic record
Abstract
Worms have played a more important part in the history of the world that most persons would at first suppose. Charles Darwin The Formation of Vegetable Mould Through the Action of Worms with Observations on their Habits (1881) When looking at depositional sequences, no one gets upset when they see a ripple mark, but the presence of a few burrows frequently will divide the field party into two factions. One group falls asleep while the other group begins a lengthy discussion on phylogeny, ontogeny, nutrient upwelling, biochemistry, and the “Voyage of the Beagle”. Jim Howard “Sedimentology and trace fossils” (1978) Jim Howard’s ironic comment elegantly illustrates both the joys and risks of practicing and communicating the science of organism–substrate interactions to a broad audience. Ichnology is a science located right at the crossroads of paleontology (and biology) and sedimentology (and stratigraphy). Trace fossils link paleontology and sedimentology in ways that most body fossils cannot achieve. In this context, ichnological investigations provide dynamic links among numerous fields. Analysis of specific ichnofaunas results in meaningful contributions to paleoecology, sedimentology, sequence stratigraphy, reservoir characterization, diagenesis, paleoclimatology, paleooceanography, biostratigraphy, evolutionary paleoecology, paleoanthropology, and archaeology. Such studies illustrate how an integrated approach that articulates ichnological information with other sources of data results in a better understanding of depositional setting, stratigraphic architecture, reservoir permeability, organism behavior, and ecosystem reconstruction and evolution. Thus, a multifaceted approach to ichnology will help bridge the gap between biologists and geologists, as well as between theoretical frameworks and applications. Because of this close link between ichnology and several other fields, we will often visit some of these neighboring disciplines in search for connections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.585 | 0.427 |
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".