Bronze industry of the Late and Final Bronze Age fromploughsoil in the borderland between eastern Bohemia andnorthwestern Moravia
Bibliographic record
Abstract
Univerzita Karlova v Praze Filozofická fakulta Ústav pro archeologii Disertaèní práce Svazek I PhDr. David Vích Bronzová industrie mladší a pozdní doby bronzové z ornice na pomezí východních Èech a severozápadní Moravy Bronze industry of the Late and Final Bronze Age from ploughsoil in the borderland between eastern Bohemia and northwestern Moravia 2023 Vedoucí práce: doc. PhDr. Luboš Jiráò, CSc. Abstract: By means of detector prospecting in the agriculturally cultivated areas on the border of eastern Bohemia and north-western Moravia for the Late and Final Bronze Age, archaeological sources numbering almost one thousand pieces weighing more than 11 kg were collected in the years 2005-2015. The most frequent (33.5 %) is evidence of metalwork. This is followed by the category of tools (27.7%) with a significant proportion of needles (15.1%) and axes (7.8%) and jewellery (18.8%). 6.6% of artefacts belong to militaria. Other finding categories remain quite marginal. If we focus only on finds from the settlement sites, omitting evidence of metalwork, just under half of the artefacts recovered (42%) are tools, with a predominance of needles followed by axes, and more than a quarter of the artefacts recovered from the settlements are jewellery (27%), with a marked predominance of needles. Circular jewellery also...
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".