Novel Sampling Methodology for Identifying Presence and Absence of Aquatic Macrophyte Species in Two Lakes in Northern British Columbia, Canada
Bibliographic record
Abstract
Aquatic macrophytes provide essential food and habitat for all levels of aquatic life, as well as have a critical role in nutrient cycling. Many aquatic macrophytes are submerged for part or all of their life cycle, which makes them difficult, time-consuming, and expensive to sample. During a study of two lakes, Bednesti and Berman, in northern British Columbia, Canada, we developed a nondestructive, cost-effective, and time-sensitive sampling methodology for aquatic macrophytes. With two people sampling in a single boat, 90 randomly selected sample sites with four transects each were completed over five 4-hr sampling periods. This methodology produced presence/absence data, which would be an effective methodology for monitoring aquatic macrophyte populations in freshwater environments. The technique allowed us to identify aquatic macrophytes at a species level, regardless of emergent or submergent growing patterns. This technique was used to study the impacts of residential development on freshwater aquatic macrophyte communities and provided useful and easily obtainable data for that purpose. Natural resource and conservation fields may find this technique useful to monitor aquatic environments for specific, rare, or invasive aquatic macrophyte species in an efficient and cost-effective manner.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".