Evaluating the Interpretation Uncertainty From the Manual Streambank Delineation
Bibliographic record
Abstract
ABSTRACT Streambank delineation enables the development of two important parameters: streambank position and channel width. For older aerial images that cannot be easily processed by automatic methods, manual interpretation of streambanks is a necessary task. In this study, the streambanks of a 70 km reach of the Canadian River in Oklahoma, USA were manually delineated by three individuals, using eight, red‐green‐blue band composite imagery. We quantified uncertainty across the three interpretations and evaluated how, sinuosity, flow rate on the image, and recent flood activity influence uncertainty. We developed two streambank calculation methodologies, namely the centroid method and direct width method. The centroid method produced three uncertainty estimates for the parameters streambank position, absolute channel width, and signed channel width, while the direct width method produced an uncertainty estimate for absolute channel width. Interpretation uncertainty was defined as the median error value for skewed distributions or as the mean value for normal distributions. Interpretation uncertainty estimates ranged from −0.1 to 2.8 m. These uncertainty values are 0.1 to 3 times the 1 m pixel image resolution and are an order of magnitude lower than the median width of the channel, which changed from 106 m in 2013 to 71 m in 2021. Increased channel sinuosity was shown to increase the median and interquartile range of the uncertainty metrics, while more outliers were observed for images taken soon after two‐year return flows. Overall, we conclude that manual delineation is a reliable method to obtain streambank position and channel width in large, geomorphically complex rivers.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".