"The Bird Almanac: A Guide to Essential Facts and Figures of the World's Birds" by David M. Bird [book review]
Bibliographic record
Abstract
Here is where the cultures and generations truly clash.And how does this relate, link and compare with what the Canadian and provincial government does (see for instance Geogratis website http://geogratis.cgdi.gc.ca/)?Here a change and update to the new millennium is needed for British Columbia so that high quality bird and conservation data are freely shared and made available to the global public over the internet nowadays.The description of the data center mentions for instance that they hold the largest nest record data pool for Canada with 180 000 records! Let's put these massive data sets on the public table for much of the urgently needed Conservation Management in British Columbia.The book jacket reads: "Perfect for Birdwatchers, Naturalists and Environmentalists."The reader might decide him-or herself on the philosophical question how much birds can and should be used to address environmental concerns, and whether they contribute to conservation.I recommend this book for sure as a very nicely written description and photographic explanation of birds in British Columbia, as well as a celebration of a Canadian and world heritage component.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.029 |
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".