Confusion Matrices Help Prevent Reader Confusion: Reply to Bechtel, B., et al. A Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment, Remote Sens. 2019, 11, 2420
Bibliographic record
Abstract
Land use/land cover (LULC) maps are now being used across disciplines for many different types of applications, e.g., to analyze urban heat islands or rainfall-runoff dynamics. Traditional map accuracy metrics are limited in this regard, as they only assess LULC map thematic accuracy. In reality, some types of misclassification lead to larger estimation errors for these specific applications. In a previous study, we developed a new map accuracy metric (referred to here as “JJ19”) to assess the accuracy of local climate zone maps for urban microclimate analysis. In the previous work, we also attempted to reproduce another metric (weighted accuracy (WA)) proposed for this purpose, but misinterpreted it due to a lack of methodological information available (principally, the lack of a confusion matrix to demonstrate how WA was derived). We sincerely thank the authors of Bechtel et al. 2019 for providing more information on WA in response to our previous study and are happy to report that we found that the metric is now both reproducible and valid. On the other hand, we found some other aspects of Bechtel et al. 2019’s study to be inaccurate, particularly their claims regarding the suitability of the JJ19 metric. Finally, we made a minor improvement to the JJ19 metric based on Bechtel et al.’s comments.
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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 | 0.027 | 0.160 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.034 | 0.071 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".