Letter from the President: Biodiversity and the Smallest Floral Kingdom
Bibliographic record
Abstract
As attention to global biodiversity heats up because of the upcoming December meeting (COP15) of the Convention on Biological Diversity in Montreal, media stories on the state of the globe’s biodiversity are becoming more numerous. Humans love lists, and someone would inevitably produce a list of countries ranked according to their biodiversity. Swiftest, a data analytics company interested in the insurance and travel industries, has recently created a country-by-country biodiversity ranking. Their list includes 201 countries (193 member countries in the UN). The list is based on a relatively simple index that counts all the species of five groups of animals – birds, amphibians, fish, mammals, and reptiles – as well as the number of plant species. Each country’s score is determined on a 0-100 scale based on the total number of each of the five animal groups, with a 0-50 scale for plants. The highest possible score is, therefore, 550. Brazil ends up on top of the list with a total score of 512.34 (a result that is not that surprising given the species richness of the Amazon basin), while San Marino (a tiny country of 61 km2 located in Italy) is at the bottom with a score of 5.47.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".