Biodiversity assessments: Origin matters
Bibliographic record
Abstract
Recent global efforts in biodiversity accounting, such as those undertaken through the Convention on Biological Diversity (CBD) and Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), are vital if we are to track conservation progress, ensure that we can address the challenges of global change, and develop powerful and scientifically sound indicators.Schlaepfer [1] proposes that we should work toward inventories of biodiversity that account for native and non-native species regardless of species origin and ecological context.We strongly disagree with the approach of combining counts of native and non-native species because this will reduce our capacity to detect the effects of non-native species on native biodiversity with potentially devastating consequences.Compelling and abundant evidence demonstrates that some non-native species can become invasive and produce major ecosystem disruptions and even native species extinction.Unfortunately, we still cannot be certain which non-native species will be the most detrimental (e.g., [2]).Combining native and non-native species together into a single biodiversity index would not only inflate biodiversity estimates and risk promoting the spread of invasive non-native species but would also
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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.034 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".