Invasion lags: The stories we tell ourselves and our inability to infer process from pattern
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
Abstract Aim Many alien species experience a lag phase between arriving in a region and becoming invasive, which can provide a valuable window of opportunity for management. Our ability to predict which species are experiencing lags has major implications for management decisions that are worth billions of dollars and that may determine the survival of some native species. To date, timing and causes of lag and release have been identified post hoc, based on historical narratives. Location Global. Methods We use a simple but realistic simulation of population spread over a fragmented landscape. To break the invasion lag, we introduce a sudden, discrete change in dispersal. Results We show that the ability to predict invasion lags is minimal even under controlled circumstances. We also show a non‐negligible risk of falsely attributing lag breaks to mechanisms based on invasion trajectories and coincidences in timing. Main conclusions We suggest that post hoc narratives may lead us to erroneously believe we can predict lags and that a precautionary approach is the only sound management practice for most alien species.
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 | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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