Fast, Nondestructive, and Cost-Effective Methods to Detect Pesticide Residues: A Case Study of Several Repatriated Karuk Tribe Artifacts
Bibliographic record
Abstract
Abstract This study describes the use of three different nondestructive methods to determine whether or not nine artifacts belonging to the Karuk Tribe had been treated with common inorganic and organic pesticide agents. A portable X-Ray Fluorescence analyzer was used to estimate the concentrations of arsenic, mercury, and lead at two different locations on each artifact. Black beads on a necklace were found to contain 2.1% lead and 0.23% arsenic, which can be attributed to the natural composition of the beads. Leather on a drum mallet was found to contain 0.49% lead and 0.10% arsenic, which were due to the pigments used to decorate this item. Microwave Plasma-Atomic Emission Spectrometry analysis of swab samples taken from the surfaces of an elk horn, bow, and musical drum showed nondetectable levels of arsenic and lead. Gas Chromatography/Mass Spectrometry analysis of a second set of swab samples taken from the surface of each artifact showed nondetectable levels of p-dichlorobenzene, naphthalene, dichlorodiphenyltrichloroethane, and other common organic pesticides. These results suggest that these artifacts were not treated with pesticides for preservation purposes, and hence they can be handled, worn, and used as intended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".