Appreciating biodiversity science: Why biodiversity should be a big science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Appreciating biodiversity science: Why biodiversity should be a big science Professor F. Guillaume Blanchet from Université de Sherbrooke posits the importance of treating biodiversity science as a big science to reach the goals set during the COP15 on biodiversity. Biodiversity encompasses every variety of life on Earth, from the bacteria that cause strep throat to blue whales and humans. Biodiversity has been studied everywhere, from seemingly alien ecosystems around hydrothermal vents at the bottom of the oceans to the mosses and lichen of Mount Everest. Biodiversity should not only be seen as the number of species that live in a particular area at a specific time but also understood as how living organisms differ in their genetics and functions and how ecosystems differ from each other.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.050 | 0.002 |
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 it