Data from: Tracking two invasions for the cost of one: Opportunistically tracking the range expansion of non-native Palaemon macrodactylus in the Salish Sea through participatory science
Bibliographic record
Abstract
While conducting early detection monitoring for invasive European green crab (Carcinus maenas), Washington Sea Grant Crab Team discovered a non-native shrimp Palaemon macrodactylus, hitherto unreported along Washington's portion of the Salish Sea. By examining prior data, and tracking this species closely over subsequent years, we were able to consistently monitor the spread and increase of this species across the region. The program and dataset enabled a highly quality-controlled and verified record of this species. Originating near the Canadian border, P. macrodactylus has spread southward into the main basins of Puget Sound and has been observed in Hood Canal as well. At sites where this shrimp has been consistently documented, the relative abundance over time has increased, indicating that the invasion has become established but not yet reached equilibrium. Similar to studies in other regions of the globe where it is found, we observed that P. macrodactylus favors estuarine habitats and demonstrates seasonal migration within creek systems. The Salish Sea population is within demographic values published for other invasions, but females achieve a smaller maximum size, and reproductive maturity at a smaller size than those reported from the native range. This might suggest the potential for an altered life history strategy favored by an introduction to a novel evolutionary context. Given the rate of spread over the last decade and the density of suitable habitat, we anticipate that P. macrodactylus will continue to expand its range within the Salish Sea. No ecological impacts of this species have been documented elsewhere. Nevertheless, this approach demonstrates the benefit of participatory science monitoring in tracking cryptic or otherwise unnoticed species invasions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.002 |
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".