Deriving inventories of non-native plant species from iNaturalist: Insights from urban centres of the Western Cape, South Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate, up-to-date inventories of non-native species are important to document and improve our understanding of biological invasions globally and inform management decisions. Traditional methods for the collation of inventories are time- and resource intensive, and lists become outdated if not regularly updated. The community science platform iNaturalist can contribute to the collation of regularly updatable (“living”) inventories of non-native species. However, robust and transparent workflows are needed to optimise data quality to take full advantage of iNaturalist. We present the semi-Automated Non-Native Inventory Compilation (sANNIC) workflow for the collation and completeness assessment of non-native vascular plant inventories from iNaturalist. The workflow is informed by the World Checklist of Vascular Plants (WCVP) and is used to compare native ranges to a reference area. The utility of the workflow is demonstrated by compiling non-native species inventories of 100 urban centres in the Western Cape province, South Africa. A total of 947 taxa of wild-growing, i.e. casual, naturalised and invasive plants were observed in these urban centres which showed varying levels of sample completeness. Most small towns had too few records for a completeness assessment. Larger urban centres and those near the coast were typically better sampled. This work highlights the potential for iNaturalist to construct non-native species inventories given sufficient coverage and thorough curation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it