Bibliographic record
Abstract
Read it on Medium (preferred) Costa Rica is home to 5% of the world’s biodiversity. Here are eight fascinating natural history facts we discovered during a visit to this incredible country. Pura vida! We spent this year’s holiday season in Costa Rica, marking our first visit to Central America — a region renowned for its incredibly rich biodiversity, with about 5% of the world’s species said to be found here. The trip exceeded all expectations. We were guided by exceptional local experts and saw new species every single day. We observed an astonishing variety of mammals, birds and reptiles. Even the insects and other invertebrates impressed, with beetles — naturally — capturing most attention. And the plants? Well, we’re not ones to suffer from plant blindness, and they certainly didn’t disappoint either. Here are eight fascinating natural history vignettes that inspired us. · 2025: the year of the snake · Parakeets: make sure to eat your termites · Sexual selection: impressing the ladies · Whip spiders: hunting with your legs · Tiger beetles: walking on stilts · From a clambering cactus to a fruit for the 21st century · Beyond leaves: bark photosynthesis keeps trees thriving · Pochote tree: titans with thorns
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".