Bibliographic record
Abstract
Introduction: point and spatial operations In this chapter we introduce a number of point and spatial operations that can be performed on continuous field data. We begin with the use of map algebra, before moving on to the calculation of derivatives (e.g. slope and aspect) and spatial filtering (e.g. smoothing and edge detection), all of which are widely used by archaeologists. In the final section we introduce more specialised techniques that have archaeological potential. Map algebra is a point operation, whereas the other techniques discussed in this chapter are spatial operations. Point operations compute the new attribute value of a location with coordinates ( x, y ) from the attribute values in other maps at the same location ( x, y ), (Fig. 9.1b). In contrast, spatial operations compute the new attribute value of a location from the attribute values in the same map, but at other locations – those in the neighbourhood (Fig. 9.1a). The neighbourhood used in a spatial operation may or may not be spatially contiguous. For example, slope is usually calculated using the elevation values in a neighbourhood comprising the four or eight map cells immediately adjacent to the location in question (see below), but we saw in Chapter 6 how inverse distance weighting interpolates elevation values from some number of nearest spot heights, irrespective of how far away those spot heights actually are.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".