Quantitative assessment of invasive species in lacustrine environments through benthic imagery analysis
Bibliographic record
Abstract
The establishment, spread, and impact of the invasive bivalve Corbicula fluminea ( C. fluminea ), in Lake Tahoe threatens native species distribution in the lake and, potentially, has long‐term implications for water clarity. In 2009, UBC‐Gavia , an Autonomous Underwater Vehicle (AUV), was used as a platform to collect georeferenced imagery of the benthic regions of Lake Tahoe to determine the lake‐wide distribution of C. fluminea . Images were collected in water depths less than 10 m at an approximately constant height above the bottom of 2 m. Images were processed using a semi‐automated procedure to determine the ratio of the lakebed covered by exposed C. fluminea shells. A visual review was conducted on a subset of the images to determine presence of filamentous algae that has been observed in association with C. fluminea . Nearly 100 km of shoreline was covered over a 7‐d period, and C. fluminea presence was reconfirmed in 4 regions and additional 10 regions identified. In regions where the presence of C. fluminea was confirmed, C. fluminea depth distribution was validated by comparing image detection counts and results from a benthic sediment grab sample survey. Three regions around the lake were identified to have filamentous green algae or charophyte species. It was impossible to identify species of the known filamentous algal taxa ( Cladophora glomerata, Spirogyra spp., and Zygnema spp.). The collected imagery provides a synoptic view on species distribution within the lake that can be used for efficient monitoring of invasive species in freshwater and saltwater bodies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".