A neutral terminology to define ‘invasive’ species
Bibliographic record
Abstract
ABSTRACT The use of simple terms to articulate ecological concepts can confuse ideological debates and undermine management efforts. This problem is particularly acute in studies of nonindigenous species, which alternatively have been called ‘exotic’, ‘introduced’, ‘invasive’ and ‘naturalised’, among others. Attempts to redefine commonly used terminology have proven difficult because authors are often partial to particular definitions. In an attempt to form a consensus on invasion terminology, we synthesize an invasional framework based on current models that break the invasion process into a series of consecutive, obligatory stages. Unlike previous efforts, we propose a neutral terminology based on this framework. This ‘stage‐based’ terminology can be used to supplement terms with ambiguous meanings (e.g. invasive, introduced, naturalized, weedy, etc.), and thereby improve clarity of future studies. This approach is based on the concept of ‘propagule pressure’ and has the additional benefit of identifying factors affecting the success of species at each stage. Under this framework, invasions can be more objectively understood as biogeographical, rather than taxonomic, phenomena; and author preferences in the use of existing terminology can be addressed. An example of this recommended protocol might be: ‘We examined distribution data to contrast the characteristics of invasive species (stages IVa and V) and noninvasive species (stages III and IVb)’.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".