Bibliographic record
Abstract
This Article examines the consequences of overharvesting biodiversity, focusing on its detrimental effects on biodiversity—on the biosphere, ecosystems, communities, populations, and individual organisms. It evaluates legal mechanisms designed to protect biodiversity from overharvesting and also considers legal mechanisms that lead to overharvesting to the detriment of biodiversity. The Article compares regulations and laws from multiple jurisdictions, including Europe, Oceania, Asia, Africa, and the Americas, and analyzes how these regulations and laws seek to curb overharvesting. Examples include the United States’s Endangered Species Act, Canada’s Wild Animal and Plant Protection Act, and the European Union’s Marine Strategy Framework Directive. International conventions such as the Convention on International Trade in Endangered Species of Wild Fauna and Flora (“CITES”) are also assessed for their success in protecting species from commercial overexploitation. Drawing from these case studies, this Article identifies best practices for preventing overharvesting and proposes strategies for more effective biodiversity conservation. These proposals necessitate the implementation of ecosystem-based approaches, adaptive environmental assessment and management techniques, and stronger regulatory enforcement to secure longevity and the survival of biodiversity despite overharvesting. This Article concludes by advocating for an international legal framework that promotes resource sustainability while maintaining biodiversity. This proposal integrates precautionary principles, cross-border cooperation, and equitable resource sharing to foster a future where human resource use no longer jeopardizes biodiversity.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".