Sensitivity of Landsat/IKONOS accuracy comparison to errors in photointerpreted reference data and variations in test point sets
Bibliographic record
Abstract
We evaluated the sensitivity of a comparison between classified Landsat and IKONOS images to the reference data used to make the comparison. Reference data were produced using two different procedures and by multiple individuals performing identical procedures. We also screened potential test points according to the homogeneity of their surrounding neighbourhoods. Landsat performed relatively better than IKONOS regardless of the reference data used, but the individual performance of each classified image varied significantly according to the reference dataset against which it was scored. The assessed ability of Landsat and IKONOS to recognize certain land‐cover classes was extremely sensitive to the reference data. Differences in individuals' interpretation habits outweighed the effects of the reference data production method, and the test points' neighbourhood homogeneity did not consistently bias overall accuracy. These results support careful documentation of uncertainty associated with reference data, including overall reliability and issues regarding particular land‐cover/use classes.
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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 | 0.047 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".