Who Needs a Plow-Zone? Using a Common Site Mapping Method in a New Way At the Silvernale Site (21GD03)
Bibliographic record
Abstract
Agricultural activities are responsible for extensive disturbance and destruction of archeological sites throughout the region and beyond. Plowing moves the artifacts from their original locations thus making it difficult to tie them back to the contexts in which they belong. It has become a relatively common practice for many archeologists when faced with this problem is to simply blade off the disturbed area of the site, usually the upper 30 to 40 centimeters, so that they can better access undisturbed areas. They do this because they believe that since the artifacts have been moved out of context that they are now useless for interpreting the site. This thesis challenges that assertion by using a common site mapping method, systematic shovel testing, in a new and more innovative way. Shovel tests were dug in a 5 x 5 meter grid across the Silvernale Site (21GD03), eventually totaling 567 shovel tests. The shovel tests were dug only to the depth of the plow-zone, usually 30-40 centimeters. After cleaning, and cataloguing the artifacts recovered from the shovel tests the data were entered into Microsoft Excel® and subsequently into the GIS Arc Map 10® computer program. Since the survey was systematically done across the village site it was possible to note concentrations of different classes of artifacts at different points in the village. These concentrations were subjected to a variety of statistical analyses and compared with the results of a geophysical survey along with the results of excavated units at the village. This method can be used to make use of previously discarded plow-zone materials to predict subsurface features at a site, such as middens, plazas, or even previously lost excavation units. It can also be used to more generally understand site patterning in cases where there are no intact subsurface features. Archeologists using this method will be able to pinpoint areas of the site that will best help them to answer specific research questions without the largely 'hit or miss' testing they would normally be subject to. Merely because a site has been cultivated for more than 100 years this does not mean that the surficial deposits lack relevant and reliable data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".