Bibliographic record
Abstract
Natural processes occurring on the surface of the earth manifest themselves at multiple spatial scales. The amount of spatial variability revealed from remote sensing images is strongly dependent on the scale of observation. Each remote sensing image is acquired at a given spatial resolution, revealing only a limited range of the existing natural variability. The simple method presented in this book was developed for the quantification of the loss of fine-scale spatial detail in low-resolution ocean color images acquired over highly patchy coastal waters. The method can however be used for the analysis of targets other than water as well as in other types of applications like the determination of the optimum scale of observation for a field sampling campaign, the design of the spatial aspects of a new sensor, study of scale-change effects in multi-scale/multi-sensor projects and possibly in many others. The current study was accomplished in 2000 for the fulfillment of a Masters of Science degree at the Remote Sensing department of Universite de Sherbrooke under the supervision of Dr. Norm O'Neill and Jean-Claude Therriault.
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.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".