Atlas of Nonindigenous Marine and Estuarine Species in the North Pacific
Bibliographic record
Abstract
(Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Marine and estuarine nonindigenous species (NIS) are a global issue, with nonindigenous species found in every ocean of the world. Effectively addressing such a global threat requires knowledge across multiple spatial scales and topics, ranging from knowledge of the habitat preferences of an invader to the global distributions of potential invaders as input into risk assessments. Over the last several decades, considerable progress has been made in understanding the number and biogeographic distribution of marine/estuarine nonindigenous species on the Pacific Coast of the United States and Canada, from Carlton's omnibus doctoral dissertation on the San Francisco Estuary (Carlton, 1979) to the formation of the Canadian Aquatic Invasive Species Network (CAISN, http://www.caisn.ca/en/) and a monograph of invaders in Hawaii (Carlton and Eldredge, 2009). Although the extent of earlier research does not appear to be as extensive in Asian countries, a number of recent studies indicate a growing recognition of the economic, health and environmental threat of near-coastal invaders (e.g., Iwasaki, 2006, Otani, 2006; Seo and Lee, 2008; Chavanich et al., 2010; Doi et al., 2011; Zvyagintsev et al., 2011). While these and many other studies provide critical information for specific species, locations, or countries, what has been lacking is a comprehensive analysis of near-coastal invaders at the North Pacific scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".