Teaching while selecting images for satellite-based forest mapping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Satellite images are increasingly being used to monitor environmental temporal changes. The general approach is to compare old images to recent ones acquired from a satellite in order to detect changes that occurred during the period between which these images were taken. An important step in this overall process is the acquisition of image data that will best allow assessing the temporal changes. This acquisition requires some expertise in order to select images from satellite sensors that use the appropriate spectral band for a forest application. For instance, a sensor suitable for classifying urban images will not necessarily be appropriate for forest mapping, because buildings reflect electromagnetic waves differently from trees and hence show a different spectrum with the same sensor. In this paper, we present an emerging Image Data Selection Assistant (IDSA) that uses an expert system combined with intelligent tutoring to help users in choosing images for updating forest maps, while at the same time teaching them how to best select images depending on the task.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it