Quantifying the temporal dynamics of wood in large rivers: field trials of wood surveying, dating, tracking, and monitoring techniques
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Wood plays an important role in stream ecology and geomorphology. Previous studies of wood in rivers have quantified spatial distributions but temporal dynamics remain poorly documented. The lack of such data is related to limitations of existing methods, especially when applied to large rivers. Five techniques are field‐tested to assess their utility for quantifying the temporal dynamics in rivers: repeated high‐resolution aerial surveys, the measurement of wood physical characteristics as proxies for 14 C dating, passive and active radio frequency identification (RFID) tags, radio transmitters, and video. The spatial distribution of wood is surveyed using aerial imagery with a resolution finer than 0·10 m. The estimation of temporal trends by repeated aerial‐based surveys needs to consider vegetation growth and hiding. Wood residence times can be calculated using 14 C analysis, but the assessment of wood physical characteristics including decay status and wood density offers a cheaper, if less accurate, alternative. Wood resistance to penetration is tested but results are not significant. Radio transmitters are reliable for multi‐year (∼5 year) surveys and can be detected at 800 m. Passive RFID tags are limited by a read range of 0·30 m but are reliable for longer term (>5 year) studies. Active RFID tags combine a moderate read range (10–300 m) and low cost with in‐flood detection but require more testing. Video monitoring of wood passing on the surface of a river is successfully implemented. For a single flood on the Ain River (France), wood transport rates are an order of magnitude higher on the rising limb of the hydrograph than on the falling limb. Overall, the techniques improve the ability to gather the data needed to understand wood transfer processes and calibrate budgets of wood in rivers. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 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 it