Wildlife collection for scientific purposes
Bibliographic record
Abstract
Illegal transfer of wildlife has 2 main purposes: trade and scientific research. Trade is the most common, whereas scientific research is much less common and unprofitable, yet still important. Biopiracy in science is often neglected despite that many researchers encounter it during their careers. The use of illegally acquired specimens is detected in different research fields, from scientists bioprospecting for new pharmacological substances, to taxonomists working on natural history collections, to researchers working in zoos, aquariums, and botanical gardens. The practice can be due to a lack of knowledge about the permit requirements in different countries or, probably most often, to the generally high level of bureaucracy associated with rule compliance. Significant regulatory filters to avoid biopiracy can be provided by different stakeholders. Natural history collection hosts should adopt strict codes of conduct; editors of scientific publications should require authors to declare that all studied specimens were acquired legally and to cite museum catalog numbers as guarantee of best practices. Scientific societies should actively encourage publication in peer-reviewed journals of work in which specimens collected from the wild were used. The International Commission on Zoological Nomenclature could require newly designated types based on recently collected specimens to be accompanied by statements of deposition in recognized scientific or educational institutions. We also propose the creation of an online platform that gathers information about environmental regulations and permits required for scientific activities in different countries and respective responsible governmental agencies and the simplification of the bureaucracy related to regulating scientific activities. This would make regulations more agile and easier to comply with. The global biodiversity crisis means data need to be collected ever faster, but biopiracy is not the answer and undermines the credibility of science and researchers. It is critical to find a modus vivendi that promotes compliance with regulations and scientific progress.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".