The unreliable concept of native range as applied to the distribution of the rusty crayfish (Faxonius rusticus) in North America
Bibliographic record
Abstract
Abstract The concept of native range in invasion biology is difficult to define since, in many cases, this type of range is unknown and cannot be determined. We investigate the uncertainties related to this concept by focusing on the distribution ofFaxonius rusticus(Girard, 1852), also known as the rusty crayfish, which is perceived as possibly the worst invasive crayfish species in North America. In this study, we undertake a comprehensive literature review, which includes 430 studies published between 1852 and 2018, in order to analyze the native and introduced ranges of this species. The rusty crayfish was reported to occur in 33 states in the U.S.A. and 3 Canadian Provinces. Ten of these U.S. states and one Canadian Province have been included multiple times in both the native and the non-native ranges of this crayfish. The confusion regarding the limits and history of the native range of the rusty crayfish has implications for the conservation of this species in various jurisdictions. This review also demonstrates that even for intensely studied species perceived as invasive, we often do not have a clear understanding of essential concepts such as native and non-native range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| 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".