Archaeological Predictive Model – Manning Diversified Forest Products Ltd., P15 Forest Management Unit
Bibliographic record
Abstract
This document describes an archaeological predictive model prepared for Manning Diversified Forest Products Ltd. (MDFP) of Alberta. The model refers to the P15 Forest Management Unit (FMU) and is for use in conjunction with their Historical Resource Management System. Multi-criteria evaluation was used to create the model. The modeling process involved the incorporation of data layers including Alberta Vegetation Inventory (AVI) layers from MDFP, a 25-m digital elevation model (DEM), topographic features, a cost raster of values indicating ease of travel, and terrain roughness. The FMU was stratified into two areas to ensure greater internal similarity. The model identified high-potential areas, in which archaeological sites can be expected, and no-potential areas, in which they would be expected to be absent. The next step should be to test the accuracy of the model. Following an initial testing period, changes may be made to increase precision. Increases in accuracy will result from verifications in the accuracy of the predictions, archaeological sites being found in areas identified in the model as having high potential and no sites found in areas of no potential. Increases in the accuracy of the model will also result with the incorporation of better resolution base mapping 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".