Benthic video image analysis facilitates monitoring of Dreissena populations across spatial scales
Bibliographic record
Abstract
In contrast to marine systems where remote sensing methods in studies of benthic organisms have been widely used for decades, these methods have experienced limited use in studies of freshwater benthos due to the general lack of large epifauna. The situation has changed with the introduction of dreissenid bivalves capable of creating visible aggregations on lake bottoms into North American freshwaters in the 1980s and 1990s. The need for assessment of Dreissena densities prompted exploration of videography as a potentially cost-effective tool. We developed a novel sampling method that analyzes video recorded using a GoPro camera mounted to a benthic sled to estimate Dreissena coverage, density, and biomass over relatively large areas of the lake bed in the Laurentian Great Lakes compared to traditional sampling methods. Using this method, we compared quagga mussel coverage, density, and biomass estimates based on three replicate Ponar grabs vs. 500 m-long video transects across 43 stations sampled in Lake Michigan in 2015. Our results showed that analysis of images from video transects dramatically increased the bottom area surveyed compared to Ponar grabs and increased the precision of Dreissena density and biomass estimations at monitoring stations. By substantially increasing the ability to detect relatively small (<20%) changes between years within a particular station, this method could be a useful and cost-effective addition for monitoring Dreissena populations in the Great Lakes and other freshwater systems where they occur.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".