Multi‐proxy geoarchaeological study redefines understanding of the paleocoastlines and ancient harbours of Liman Tepe (Iskele, Turkey)
Bibliographic record
Abstract
Abstract Determining the position of Liman Tepe's (ancient `Clazomenae') archaeological features relative to the coastline is important for understanding their intended function and reconstructing the character of Aegean maritime activities and sea‐based trade. Previous attempts at reconstructing harbour locations at Liman Tepe relied on extrapolating paleoenvironments based on modern surface topography. In light of this, samples from a sediment coring survey and terrestrial and underwater archaeological excavations were analysed using multi‐proxy geoarchaeological methods to determine paleoenvironmental facies. Micropaleontological (foraminifera), sedimentological (grain‐size analysis) and geochemical (δ13C/δ18O) analyses resulted in the reconstruction of the coastal paleogeomorphology, including the presence and absence of ancient harbouring areas. Neither of the previous coastal reconstructions was supported by the new results. Instead, two separate harbouring areas were recognized, one coincident with the Early Bronze Age (4800–3900 years bp) and a second during the archaic and classical periods (c. 2800–2400 years bp). These results emphasize the necessity for multi‐proxy geoarchaeological studies when approaching coastal archaeological sites as a means to reconstruct paleocoastal geomorphology and understand ancient maritime development better.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".