A Checklist of the Naturalized Vascular Plants of Western Australia II: Changes 1994-2004
Bibliographic record
Abstract
Weed numbers continue to increase at a steady rate, but explanations of the causes are often lacking.Checklists of naturalised plants for Western Australia produced in 1994, 1999 and 2004 were used to collate the reasons for these new records.Between these checklists the number of recorded naturalised taxa rose from 1,073 to 1,234.The major causes of these differences were increased survey effort which added 105 previously unrecorded weeds.Literature survey / taxonomic revisions added another 95, highlighting the importance of timely taxonomic studies of collections of weeds and the need to systematically incorporate this information into databases.During this period 47 species previously listed as naturalised were deleted, but 94 taxa on the verge of becoming naturalised were also added as garden escapes.Despite many "new" records being the result of increased taxonomic effort, the number of naturalised species continues to increase at a steady rate (the number of completely new records occurring at a rate of approximately 10 per year over the survey period).Approximately 70% of these new weeds were deliberately introduced as ornamentals or for agriculture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".