To Explore The Collective Animal Erratic Panic and Biomimetics
Bibliographic record
Abstract
In biomimetics, we learn and get inspiration from animals to improve our quality of life. It is important to have better understanding on what, why, and how animal did so we can apply the biomimetics more effectively. Anomalous behaviors including the collective animal erratic panic (CAEP) are some of the poorly understood and potentially very important and inspiring phenomena. CAEP is not commonly noted. But it is often noticed before, during, or right after some abrupt natural disasters. There are many speculations, confusions and controversy associated with the still mysterious CAEP. CAEP could provide us with invaluable inspiration to improve our biomimetics including sensing and signal processing. CAEP would also help us to reduce our loss in lives and properties through detecting the precursors of the forthcoming natural disasters. We have explored the important issues on 1.What is CAEP? 2. What are the major stimuli and essential mechanisms in CAEP? and 3. What wisdoms can we gain from CAEP for better understanding and to further advance our biomimetics?We have made good advances in all three critical issues. With our preliminary results, we can explain the nearly no animal casualty in the 2004 Indian Ocean tsunami tragedy, the successful early warning in the 1975 Haicheng earthquake. We can also shed some light to the sudden disappearing of the unusually large gathering of sea lions at Pier 39 in San Francisco during 2009. Furthermore, we can fix the challenging twists of some anomalous animal behaviors in 2008 Wenchuan earthquake. Improved modeling and helpful appropriate experiments are needed to make further good advances. A better grasp of the CAEP can help us to improve our wisdom in biomimetics. It can also provide us with potentially vital systems for early warning of the deadly abrupt natural disasters. Keywords: biomimetics; panic; collective behaviour; animal; stimuli
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".