Globalization, biological invasions, and ecosystem changes in North America's Great Lakes
Bibliographic record
Abstract
INTRODUCTION Globalization, in the context of biological invasions, is the increased movement of species around the world. In this chapter, non-indigenous species (NIS) are defined as taxa moved from one geographic location of the world to another from which they were historically absent. The largest geographic barriers to species dispersal, the world's oceans, have been circumvented by the development of a global economy. Increased demand for and transport of goods has resulted in the transfer – both intentional and unintentional – of NIS on unprecedented scales. For example, colonization rates of European crustaceans in North America are estimated to be 50 000 times background levels associated with natural dispersal (Hebert and Cristescu 2002). A number of dispersal vectors are responsible for transport of aquatic NIS, though transoceanic shipping has played a particularly important role as the global economy has expanded. Establishment of NIS represents one of the most significant threats to the world's indigenous biota (Mooney and Drake 1989; Mack et al . 2000), in addition to adverse ecological and economic effects that they impart on lakes throughout the world (e.g., Hall and Mills 2000). For example, establishment of Nile perch ( Lates niloticus ) in Lake Victoria and peacock bass ( Cichla ocellaris ) in Gatun Lake resulted in extirpation or decline of native fish species (Zaret and Paine 1973; Ogutu-Ohwaya 1990; Witte et al . 1992).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".