Photo-Interpretation Keys Expert System (PIKES)
Bibliographic record
Abstract
Photo and image interpretation is a skill which incorporates the experience of the interpreter, knowledge of the object being delineated and its environment, and visual clues within an image for locating and identifying the nature of objects. Although photo-interpretation has been used since the early days of aerial reconnaissance, it has proven difficult to computerize this technique. While image enhancement and classification techniques can provide additional visual clues to the interpreters, it was not until the development of symbolic reasoning, such as exist in expert systems, that an intelligent interpreter assistant type of computer program were considered to assist the interpreter's reasoning process in a logical manner. The Canada Centre for Remote Sensing, together with MacDonald Dettwiler and Associates Ltd., has implemented image interpreter assistant system called PIKES (Photo-Interpretation Keys Expert System). PIKES is intended to operate as a tutor to teach novice interpreters the art of photo and image interpretation, and as an intelligent assistant to seasoned interpreters. It will guide the interpretation process by presenting image keys relevant to the image being interpreted. This methodology constrains the solution space for the interpreter. The concept, the design and the architecture of PIKES are described in detail.>
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.041 |
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".