A refitting experiment on long bone identification
Bibliographic record
Abstract
Abstract Refitting is an important analytical tool in archaeology that can yield valuable information on site formation processes and on the range of activities practiced at a site, including tool production, tool curation, and discard behavior, among others. In the present paper, we use refit data from a control assemblage of red deer (Cervus elaphus) long bones to assess problems of specimen identification and representation in an experiment where bones were processed for marrow. Three goals motivated this experiment: (i) to assess how different methods of NISP (number of identified specimens) calculation affect comparisons of the relative abundances of long bone regions, (ii) to evaluate whether long bone shaft regions vary with respect to the probability of identification, and (iii) to ascertain the potential refit rate for a well‐preserved and fully‐collected sample of faunal specimens. Our results show no statistical differences in terms of patterns of skeletal representation between the two methods of NISP calculation (single vs. multiple NISP counts) that we assessed. Our data also indicate that the shape, particularly the cross‐section, of fragments clearly impacts the probability of identification and refitting. Moreover, the refitting experiment reveals that, in ideal conditions, a majority of specimens (>95%) from the NISP sample can be refitted, which leads to largely reconstructed skeletal elements. Thus, the comparatively very low refit rates recorded in archaeological sites, including samples that are well preserved, suggest that the often limited extent of excavations, along with offsite discard and/or extensive sharing of parts, substantially reduce the possibility of finding refits in a faunal sample.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".