Comparison of function‐ and structure‐based schemes for classification of remotely sensed data
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
The aim of this paper is to determine how classification‐scheme information content influences remote sensing classification accuracies. Two important informational constructs in environmental science are ‘process’ and ‘pattern’. In remote sensing these are analogous to ‘function’ and ‘structure’, ‘land use’ and ‘land cover’, or ‘informational’ and ‘spectral’ classes. The objective of this research was to test the hypothesis that structure‐based classes result in extraction of more accurate information than do function‐based classes. Two hierarchical, 19‐class schemes, one functional, the other structural, were developed for application with Satellite pour l'Observation de la Terre (SPOT) multispectral data for a watershed in North Sulawesi, Indonesia. Eight of the 19 classes were shared between the two schemes since these constituted equally valid functional and structural classes. Results indicate that there is no significant difference in classification accuracy between the functional and structural classifications as a whole (Khat = 82.2% and 84.9%, respectively). However, comparison of the two sub‐matrices associated with the 11 non‐shared classes showed significantly higher accuracies for the structural classes (Khat = 91.0%) than for the functional classes (Khat = 84.1%), thereby supporting the original hypothesis. Results demonstrate that careful consideration is required when developing function‐based classes for the extraction of thematic information from remote sensor data.
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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 | 0.000 | 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 | 0.000 | 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