The impact of AI bots and crawlers on open repositories: Results of a COAR survey, April 2025
Bibliographic record
Abstract
There are a growing number of AI bots crawling repositories. These automated bots, or crawlers, navigate the internet, gathering data and indexing information for search engines, AI and large language models, and other purposes. While some bots are rather innocuous, others are sufficiently aggressive that they are increasingly causing service disruptions in repositories (and other scholarly communications infrastructures). To learn more about the current state and gain a better understanding about the impact of bots and crawlers on repositories, COAR distributed a survey to members in April 2025. The survey received 66 responses from repositories around the world (22 from Canada and US, 22 from Europe, 9 from Latin America, 6 from Asia, 4 from Australasia, 2 from Africa, and 1 unknown).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".