Bibliographic record
Abstract
The 6th International Eurasian Ornithologycal Congress was held in Heidelberg, Germany in April 23–27, 2018. Russia has tightened responsibility for online trade in rare animals. The 27th International Ornithology Congress (IOCongress2018) was held in Vancouver (British Columbia, Canada) in August 19–26, 2018. The 6th International Ornithological Conference “Modern Problems of Ornithology in Siberia and Central Asia” was held on October 18 in Irkutsk (Russia). The Round Table “Death of rare species of birds of prey on overhead power lines (OHPL): problems and possible solutions” was held at the Hotel Rixos President Astana (Astana, Kazakhstan) on November 6, 2018. Annual Meeting of the Raptor Research Foundation (RRF) was held in Skukuza Camp, Kruger National Park, South Africa in November 12–16, 2018. The Journal of Raptor Research is planning a special issue on the topic of conservation and management of raptors on overhead electric systems. Next Annual Meeting of the Raptor Research Foundation will be held in Fort Collins, Colorado, USA in 5–9 November 2019. The third round table on falconry will be held within the 8th International Conference “Diversity of hunting animals and hunting sector in Russia”, which takes place February 21–22, 2019.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.494 | 0.240 |
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".