Bias of reduced‐effort community surveys for adult Odonata of lentic waters
Bibliographic record
Abstract
Abstract. 1. Repeat surveys are needed to capture a representative spectrum of adult odonate richness at a site, but specifics on frequency and duration of surveys and associated inferential biases are poorly understood. 2. Weekly 1 h surveys of mature male dragonflies and damselflies were repeated at least 15 times at 19 ponds, lakes and wetlands scattered throughout North America. For each site, we tallied the data remaining when the weekly frequency was reduced to 75% (every 1.5 weeks), 50% (biweekly), 33% (triweekly), and 25% (monthly) and the 1 h survey to 50, 40, 30, 20 and 10 min subsets. 3. Reducing the original effort by half (i.e. to 30 min biweekly) retained about 80% of the species on average. The smallest effort (10 min monthly) retained about 49% of species. The greatest rate of information loss occurred between 20 and 10 min. 4. Across‐site analysis found that data subsets correlated to the original data set ( r > 0.81) despite up to 50% species loss. Strong correlations ( r ≥ 0.98) remained with 10–15% species loss. 5. Biweekly surveys lasting 20–40 min each may provide a representative and cost‐effective sample of adult odonate richness in lentic study sites. Losing a handful of species should not greatly undermine richness and compositional comparisons among sites.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".