How to Prepare for Geoforensic Fieldwork to Investigate Archaeological Resource Crime
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Geoforensic analyses complement archaeological resource crime investigations, cultural resource damage assessments, and other investigations involving sediments. Civil and criminal litigation may hinge on attributions of sediments recovered from persons, equipment, objects, and localities to specific source deposits, including altered cultural resources. Geoforensic fieldwork often entails fluid interplays among geological, archaeological, and investigative factors, and few scientists have experience working in such contexts. Geoforensic specialists may be tasked to swiftly investigate unfamiliar regions to obtain representative specimens and to present expert reports grounded in scientifically reliable principles and methods. For these reasons, systematic preparation is needed to improve geoforensic fieldwork effectiveness and efficiency. We present recommended procedures and field-tested assets for five pre-fieldwork steps: (1) commit to the teamwork, discretion, and professionalism required for crime scene investigation and case resolution; (2) gather geological and archaeological background information; (3) assemble the sediment sampling tool kit; (4) prepare sediment sampling documentation and specimen collection forms; and (5) obtain necessary permits and law enforcement, landowner, or attorney guidance for participation in crime scene reconnaissance, survey, or resurvey. Completion of these five steps will optimize the prospects for geoforensic contributions to cultural resource damage assessments and to just resolution and remediation of unauthorized cultural resource alterations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it