<b>The global trade of rarity</b>
Bibliographic record
Abstract
The script run analyses of the Global trade of rare species of Orchids. The File is a R script containing the most relevants analyses. Data wrangling was performed using script "exploration" and "Taxonomic curation". In "orc_22", the analyses and figure's scripts to reproduce the resullts of the paper.<br><br>Novelty is highly valued in global wildlife trade, with small-range and newly described species often particularly sought-after. Yet current regulations are insufficient to trace the origin and extent of sensitive species in global trade. Focusing on orchid trade under the Convention on International Trade in Endangered Species-CITES between 1977-2022, we found that Colombia and Ecuador are export hubs of native species. Whilst, 705 species were never traded from their native countries, with Germany, Canada, Netherlands, and South Korea key exporters, and USA a key importer of non-native species. Single-country endemics dominate species-level trade, whilst 155 newly described species entered trade within 2-years of description. Concerningly, 248.6 million individuals have been traded under ambiguous genus- or family-level names. This highlights systematic issues hampering the transparency, traceability, and regulation of international trade, and future challenges for ensuring native range countries receive adequate compensation through Access and Benefit Sharing agreements.<br>
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 | 0.002 |
| 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) | 0.033 | 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".