Observer‐bias and sampling uncertainties in riverine wood flux and volume estimation from video monitoring technique
Bibliographic record
Abstract
Abstract Wood is an integral part of rivers that can have both positive and negative impacts on natural systems and infrastructures. Different techniques have been developed to quantify wood flux or discharge in rivers. Among them, the stream‐side video monitoring technique has proven effective for at‐a‐station wood monitoring with a high temporal and spatial resolution over an indefinite time period. However, the visual annotation of wood pieces in the videos is subject to uncertainties due to observer bias or ‘vision limitations’, and video sampling or ‘time limitations. Vision limitations mean that there are patches in the recorded image that may or may not be considered as wood pieces depending on the judgement of the observer. Time limitations mean that the video record may be sampled to estimate the wood flux rather than completing a census of the full record due to the time‐consuming nature of continuous visual annotation. To assess these uncertainties, six flood events and 13 video segments corresponding to more than 37 days and 64,000 pieces of wood were analysed on two different rivers (Ain and Allier Rivers in France). The results show that while there is a significant difference between observers for the detection of small wood pieces (< 1 m in length), no significant difference exists for the detection of large wood pieces (> 1 m in length). The application of a truncation length (i.e., considering only wood pieces with a size higher than a certain threshold) reduces the piece number uncertainty significantly without resulting in a meaningful change in the total volume of wood. For the time limitation, it is shown that sampling uncertainty depends on wood flux related to water discharge and flood stages (rising versus falling), so a dynamic sampling strategy that depends on flood stage is recommended.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".