Ground truth validated 3D electrical resistivity imaging of the archaeological deposits at Arma Veirana cave (northern Italy)
Bibliographic record
Abstract
ABSTRACT We present 3D electrical resistivity tomography (ERT) imaging of the archaeological deposits at Arma Veirana cave (northern Italy), to date only partially explored. The archaeological importance of the cave is due to the presence of a rich Mousterian layer, traces of Late Upper Palaeolithic (Epigravettian) temporary occupations and an Early Mesolithic burial of a female newborn. ERT is rarely employed in Palaeolithic cave contexts because Palaeolithic remains are typically disseminated in loose deposits and either do not possess high electrical resistivity contrasts or are too small to be detected. Furthermore, some issues can derive from the confined environment in caves. In this view, our study represents an opportunity to assess the capability of this geophysical method to retrieve subsurface information of Palaeolithic cave deposits and create a framework for the improvement of ERT applications in such a peculiar cave context. The aim of this study was to define the features of the deposits (i.e. geometry, thickness and sediment distribution) and to map the morphology of the underlying bedrock. Results reveal that the thickness of the deposits varies both along the primary axis of the cave and transverse to it. This study allowed the recognition of shallow, meter‐sized, fine‐grained sediment‐filled structures with a longitudinal orientation with respect to the primary axis of the cave, as well as a possible erosional‐like structure. The cross‐validation of geophysical results with the archaeological evidence (the Early Mesolithic newborn burial and Epigravettian artefacts) confirms that the low‐resistivity unit could be the most promising from an archaeological point of view.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".