Bibliographic record
Abstract
With almost 3.8 billion people living between the Tropics of Cancer and Capricorn, the tropics are one of the fastest-growing regions in the world (Callender and Topp, 2020). Almost 99% of the people in the Tropics are considered to be living in ‘developing nations’ (United Nations Department of Economic and Social Affairs, 2019). These figures are set to grow further with many predicting that those living in the tropics will include one in every two people by 2050 and 55% of the world's children under the age of five years (State of the Tropics Report, 2014). In terms of their geographic and environmental significance, the tropics make up only 40% of the world's total surface area; however, the region hosts more than 80% of the planet's terrestrial biodiversity and more than 95% of its mangrove and coral reef-based biodiversity (State of the Tropics Report, 2014). From an economic development perspective, the tropical region's economy is growing 20% faster than the Rest of the World, with many tropical nations acting as key contributors to world trade, politics and innovation (State of the Tropics Report, 2020). Nonetheless, only a little more than 17% of the world's gross national product is generated in the tropics, with the vast majority of economic activity, some 65%, occurring in more temperate climates (State of the Tropics Report, 2020).
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.121 | 0.038 |
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".