Historical air photo missions in the Maritimes during the early 1920s: coverage, thematic scope, and utility 100 years later
Bibliographic record
Abstract
The historical and technological developments of powered flight and aerial photography have early connections in the Maritimes. Following the Great War (1914-18), a series of pioneering survey missions were initiated by the Canada Air Board in the civilian domain. From a science perspective, the air photos offer a unique opportunity for the detection of environmental change at an unusual centennial time scale. The missions of the early 1920s initially relied on military surplus seaplanes and innovative camera equipment that yielded several thousand high-resolution vertical air photos. This paper is focussed on the scope and outcome of the first experiments carried out over Nova Scotia and New Brunswick between 1921 and 1925, prior to more systematic use for topographic mapping during the remainder of that decade. The research is based on archival records and partial reconstruction of the digitized air photos into image mosaics. Photo interpretation and comparison with recent high-resolution satellite imagery offer insights concerning land use and land cover changes, coastal dynamics, and transformation of urban, rural and industrial landscapes. Experience todate with these early air photos and mosaics of the Maritimes holds promise for examining similar aerial survey missions in other parts of Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".