Effort and potential efficiencies for aquatic non-native species early detection
Bibliographic record
Abstract
Our objective was to determine the effort required for high-probability early detection of non-native zooplankton, benthic invertebrates, and fish using Duluth–Superior Harbor — a Great Lakes port under intense non-native species introduction pressure — as a case study. Initially, we allocated samples using a spatially balanced random design. We then resampled the harbor, but allocated samples to a few targeted areas. We detected 21 non-native invertebrate and 10 non-native fish species; however, many rare zooplankton and benthic invertebrates were likely missed. The two designs did not have significantly different species accumulation curves, but the targeted area design samples had higher species richness and detected non-native species with a significantly higher probability. It was possible to reduce the effort required to detect established non-native species. In contrast, the effort required to detect an ultra-rare, newly arrived species remained large. Based on statistical estimation theory, the effort required to detect 95% or more of species present could exceed enumerating 750 zooplankton samples (~500 000 individuals, ~90 species), 150 benthic invertebrate samples (~100 000 individuals, ~250 species), and 100 fish samples (~75 000 individuals, ~40 species).
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".