Omission and Commission Errors in the Field Mapping of Linear Boundary Features: Implications for the Interpretation of Maps and Organization of Surveys
Bibliographic record
Abstract
Phase 1 mapping has been used widely in the UK as a method of resource inventory, and as an aid to conservation management and planning. Phase 1 maps may also provide baseline information for studies of land use change by future generations of landscape ecologists and historians. Contemporary assessments of their accuracy are essential to allow their value to be judged both now and decades hence. The accuracy of Phase 1 mapping of man-made linear boundary features was quantified by comparing maps drawn by six experienced field surveyors with a ground-truth version correctly showing all features. Overall errors within maps varied from 11.2% to 96.9% between surveys. Most of the error was caused by the omission of boundaries, rather than the misclassification of boundaries whose presence was recorded (i.e. errors of commission). The likelihood of a boundary being mapped was positively related to its length, and walls were more likely to be mapped than fences. Linear features can be mapped accurately, but reliance on the discretion of the surveyors, and their interpretation of the survey manual, resulted in variable practice and incomplete data in all cases. If data on linear features are not required, the time saved could be used to improve the accuracy of mapping other habitats (a concern identified in other studies). In addition to the provision of more explicit guidance to surveyors, the reporting of estimates of mapping accuracy and precision are identified as important aspects of the survey technique which require greater attention than is currently the case.
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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 | 0.289 | 0.698 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".