Combining quadrat, rake and echosounding to estimate submerged aquatic vegetation biomass at the ecosystem scale
Bibliographic record
Abstract
Abstract Measuring freshwater submerged aquatic (SAV) biomass at large spatial scales is challenging and no single technique can cost effectively accomplish this while maintaining accuracy. We propose to combine and intercalibrate accurate quadrat-scuba diver technique, fast rake sampling and large scale echosounding. We found that the relationship between quadrat and rake biomass is moderately strong (R 2 = 0.62, RMSECV = 2.19 g/m 2 ) and varies with substrate type and SAV growth form. Rake biomass was also successfully estimated from biovolume 10 and its error (R 2 = 0.53, RMSECV = 5.95 g/m 2 ), a biomass proxy derived from echosounding, at a resolution of 10 m radius from rake sampling point. However, the relationship was affected by SAV growth form, depth, acoustic data quality and wind conditions. Sequential application of calibrations yielded predictions in agreement with quadrat observations, but echosounding predictions underestimated biomass in shallow areas (< 1.5 m) while outperforming point estimation in deep areas (> 3 m). Whole-system biomass was more accurately estimated by calibrated echosounding than rake point surveys, owing to the large sample size and better representation of spatial heterogeneity of echosounding. We recommend developing as a one-time event a series of quadrat and rake calibration equations for each growth form and substrate type. Because the relationship between biovolume and biomass depends on SAV growth form, rake and echosounding calibration needs to be conducted frequently. With the two calibrations, rake can thus be used as a rapid ground truthing or in shallow areas where echosounding is inadequate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".