Bibliographic record
Abstract
And what had he felt, I asked Mario, when he’d seen it there, the huella ? “One thing is to see artifacts presumably made by somebody and another is to see the pisada someone made, what their foot left in the earth. That’s what gives you the sense of humanity, right?” Ariel Dorfman Desert Memories (2004) While the previous chapter deals with processes occurring at the scale of deep time, we now move into a more recent past, a time witnessing human activities. For the implications of trace fossils in paleoanthropology, information is based on the study of human fossil footprints (Kim et al ., 2008a). Human footprints also play a major role in archaeology, although sources of information are found in many other ichnological datasets, such as bioerosion and bioturbation structures, and other vertebrate tracks as well (Baucon et al ., 2008). The aim of this chapter is to review recent research in the area of ichnological applications in paleoanthropology and archaeology. The first half of the chapter will be devoted to review the fossil record of human footprints, from the Pliocene to the Holocene. The second half will explore the uses of ichnology in archaeology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".