We Must all Pay More Attention to Rigor in Accuracy Assessment: Additional Comment to “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7, 8368–8390
Bibliographic record
Abstract
Despite recent calls for statistically robust and transparent accuracy assessment [1], further attention to rigor is still needed. Here I take the opportunity of a disputed accuracy assessment recently published in Remote Sensing [2,3,4] to highlight some issues regarding sampling design, response design and analysis that I often find as a reviewer, and that I too have neglected in the past, among them: (i) use of a sampling design that is purposive instead of probability based; (ii) use of suboptimal label allocation procedures for reference data; and (iii) incorrect computation of accuracy estimates and lack of reporting of confidence intervals. Following the good practices in [1] will enhance the scientific credibility of our discipline.
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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 | 0.051 | 0.342 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.055 | 0.063 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".