Integrated geophysical study in the cemetery of Marquis of Haihun
Bibliographic record
Abstract
Abstract A group of Han dynasty tombs were found in a small town in central eastern China. It is difficult to infer the underground structures of the cemetery based on surface information. Geophysical methods are nonintrusive technology that can be used in archaeological excavations to provide important underground information. Multiple geophysical methods including total magnetic field (TMF), self‐potential (SP), direct‐current resistivity (DCR) and ground‐penetrating radar (GPR) were used to investigate the structure of ancient tombs. Total magnetic anomaly (TMA) and SP data can be used to obtain the projections of anomalous bodies on earth surface. DCR and GPR data can be used to gauge and further determine the spatial locations and depths of the anomalies. The results of the integrated geophysical interpretation were used to optimize the excavation plans and were verified by the subsequent works, such as coffin chambers, dromoi, ancient building foundations and funeral pit. Integrated geophysical survey can effectively reduce the uncertainly of a single method and obtain the targets more accurately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".