Monitoring and managing the spread of marine introduced species: development of approaches and application to the European green crab («Carcinus maenas») and the Asian shore crab («Hemigrapsus sanguineus»)
Bibliographic record
Abstract
Managing introduced species, a current environmental problem, is hindered by real-world limitations of personnel, data, and funding. Monitoring is an important precursor to effective management because detecting an introduced species when its population is localized and at low density (i.e., early detection) maximizes the probability of successful eradication. Often introduced species are only detected years after the initial introduction, when eradication is no longer a viable option. Therefore, in this thesis we developed and analyzed techniques to better monitor and model the spread of the European green crab (Carcinus maenas) and the Asian shore crab (Hemigrapsus sanguineus). To overcome issues of insufficient amounts of data and personnel, we recruited nearly a thousand volunteers and validated their ability to identify introduced and native species of crabs with high levels of accuracy (Chapter 1). To increase the probability of early detection, we need to not only increase sampling intensity, but also to identify more effective and efficient sampling techniques. Therefore, we developed a quantitative, standardized experimental field approach for comparing the sensitivity of different sampling techniques for detecting organisms at low densities (Chapter 2). Even with an efficient sampling technique and increased resources of a validated volunteer monitoring network, we are still not adequately equipped for early detection monitoring on the large-scale. Since it is infeasible to monitor everywhere a species could be introduced, we should monitor where they are more likely to arrive and manage them where their impact will be greatest. To address this problem we modified an oceanographic model, incorporated biological behaviors, used extensive field data to parameterize and validate the model's ability to forecast areas that are most likely to be colonized, so we can optimally allocate our limited res
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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 | 0.000 |
| 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 | 0.001 |
| 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".