How to Prepare for Geoforensic Fieldwork to Investigate Archaeological Resource Crime
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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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.025 |
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".