Electrical Resistivity Tomography for the Modelling of Cultural Deposits and Geomophological Landscapes at Neolithic Sites: a Case Study from Southeastern Hungary
Bibliographic record
Abstract
ABSTRACT A large‐scale electrical resistivity tomography (ERT) survey was undertaken around the Neolithic tell of Szeghalom‐Kovácshalom in southeast Hungary, covering an area of almost 6 ha. High‐resolution ERT data were collected along 28 uniformly distributed transects of variable length using the roll‐along technique. A recently presented two‐dimensional fast non‐linear resistivity inversion algorithm was used to invert the ERT data and recover the true subsurface resistivity distribution along the specific cross‐sections. The algorithm calculates and stores in an efficient manner the part of the Jacobian matrix that is actually important within the inversion procedure, thus rendering it almost 4.8 times faster than the algorithm that calculates the complete Jacobian matrix, without losing quality. The algorithm was further modified to account for any non‐standard electrode configuration. A recently established iterative algorithm for sparse least squares problems (LSMR) was incorporated for the first time into the algorithm to solve the inverse resistivity problem. The effectiveness and robustness of the LSMR solver was highlighted through the processing of all the ERT lines. The processing and evaluation of the ERT data made it possible to map the thickness of the anthropogenic layer below the surface of the tell, to outline the horizontal and vertical dimensions of the palaeochannel adjacent to the tell, and to determine the general stratigraphy of geological layers up to 10 m below the ground surface. The ERT results also were used to update an older topographic map of the site showing the course of the palaeochannel around the tell. A synthetic model verified and enhanced the conclusions based on the field data. This study illustrates the added value that a systematic ERT survey can provide in reconstructing the ancient fluvial geomorphology of a microregion as well as the depth and horizontal extent of deposits associated with human habitation at archaeological sites. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".