Bibliographic record
Abstract
Policymakers and practitioners overseeing invasive species management depend on reliable research for guidance. Transparency and reproducibility are core features of reliable research, and prerequisites for outcomes to be independently replicated within the same or different systems. These features are evidently lacking in many science disciplines, including Ecology. In this discussion paper, we first report the findings of an assessment of 49 primary research studies that were part of a systematic mapping effort, showing that invasion science research exhibits the same shortfalls as ecology research more broadly. For instance, only one study explicitly considered statistical power in the methods describing study design, and only 2 studies provided access to both data and code, which is the minimum requirement for computational reproducibility. We then discuss the implications that low statistical power has for published invasion science research, for designing studies, and for policymakers and practitioners relying on primary research to inform their decisions. We then make specific recommendations, targeting the same stakeholders as well as publishers, on how to maximize the reliability of invasion science research moving forward. This includes explicitly considering and ideally estimating statistical power, undertaking a study pre-registration, making all relevant code and non-sensitive raw data accessible and useable, and devising and upholding clear and consistent policies on transparent reporting and open materials.
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 | 0.725 | 0.864 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.010 | 0.071 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".