Bibliographic record
Abstract
Settlement Archaeology, one of the sub-branches of archaeology, is actually a science based on the use of science and natural sciences to understand, describe and explain long-term cultural and behavioural changes. This branch of science, which evaluates archaeological settlements according to environmental conditions, has been accepted since the 19th century. This branch of science, which has been applied on many settlements around the world, has not been tried on the centres of the Urartu Kingdom until now. The Urartu Kingdom, which existed between the 9th and 7th centuries BC, actually achieved this long-lasting sovereignty thanks to its ability to dominate the environmental conditions. In the Van Lake Basin, the main distribution region of the kingdom, some 120 fortresses dating back to the Urartu Period have been identified. The fortresses are mostly scattered around the fertile plains and plains where agriculture and animal husbandry activities were carried out. One of the centres where the kingdom spread is the Gürpınar Plain and the lands irrigated by the Hoşap water. Thanks to the excavations and surveys carried out in Gürpınar, we have information about the Urartian settlement order. One of these important settlements is Tutmaç Stone Fortress. Located within the borders of Tutmaç Quarter of Gürpınar, the Fortress shows a complete example of a rural tribal centre architecture with a pond, a flat settlement area and a surveillance Fortress connected to the Fortress. The place of the Fortress in the settlement pattern is discussed in this study. Key Words: Settlement Archaeology, Iron Age, Urartian, Gurpınar Plain, Tutmaç Stone Fortress
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".