Geo-referenced digital photographs and videos with associated GPS waypoints and tracks for terrestrial ecosystem types from Victoria Island, King William Island and continental Nunavut, 2014
Bibliographic record
Abstract
Geo-referenced digital photographs and videos with the associated GPS waypoints and tracks of various terrestrial ecosystem types have been taken during the summer of 2014 on Victoria Island, King William Island and continental Nunavut. The photographs and videos, in combination with additional GPS waypoints and tracks, characterize site, soil and vegetation characteristics of the arctic landscape. The geo-referenced photographs and videos, as well as the GPS data will be entered in a GIS software and overlaid on satellite imagery. By matching pixel colours of the satellite images with different ecosystem types in the field, a model will be developed that will allow the assessment of ecosystem types over large areas, based solely on satellite imagery. The digital photographs were taken from ground with GPS-enabled cameras (geographical coordinates written in the image file¿s exif information). Digital videos of the landscape below were shot from helicopter and floatplane. The geographical coordinates are stored in a separate text file associated with the video file. Additionally, waypoints and tracks associated with the photographs and videos were recorded with a handheld GPS device.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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".