Study on Eco-Management Program of Status of Illegal Trade in Wildlife
Bibliographic record
Abstract
This study is dedicated to exploring the use of the "Satellite + Artificial Intelligence + Blockchain" technology project to effectively reduce the negative impacts of the illegal wildlife trade on global ecosystems, biodiversity economic security, and analysing them through a management perspective. By pre-processing a large amount of data and selecting key sub-indicators in terms of power, resources and benefits, and combining the AHP and CRITIC methods to calculate the weights and composite scores, this study identifies TRAFFIC organisations as the best performing organisations in terms of their commitment to wildlife conservation. Trends in global rainforest area, number of endangered species, and illegal trade cases were analysed using the ARIMA model, revealing a downward trend in the number of investigated cases, while an increase in illegal hunting indicators suggests an intensification of covert operations with insufficient response capacity. Therefore, the Satellite + AI + Blockchain project aims to enhance the management capacity of TRAFFIC organisations to combat illegal wildlife trade.Pearson coefficient analysis revealed a significant negative correlation (-0.973) between the incident detection rate and the actual occurrence of incidents, which was further clarified by linear regression. The likelihood of the project achieving its objectives was calculated to be 95 per cent, supported by relevant literature and model confidence levels. Finally, the study also assessed the strengths and weaknesses of the model, analysed the sensitivity of the detection rate indicator and confirmed the validity of the model assumptions. The critical role of the managerial perspective in project implementation is emphasised by exploring the impact of various aspects of PESTLE on project success.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".