Bibliographic record
Abstract
Research is an important part of the work of botanical gardens and zoos in Canada, enriching their public programs and improving management of living collections. Most of these institutions participate in research activities to some extent. Approximately a half-dozen zoos and aquaria, and a similar number of botanical gardens, have large, on-going research programs and are important centres for the training of highly-qualified personnel. About half of Canadian botanical gardens and arboreta are based at universities, and many others are municipal not-for-profit organizations. In contrast, no Canadian zoos are based at universities, although several have formal links including cross-appointed staff and active graduate programs in many disciplines. A wide range of research is undertaken by, or at, these institutions that are actively involved in a broad spectrum of conservation programs, both in situ and ex situ. Projects and programs include veterinary science, husbandry or plant propagation, animal (and visitor) behaviour, ecology, habitat rehabilitation, taxonomy, systematics, physiology and phenology. Regardless of their size, these institutions make contributions to discoveries and innovations, and provide excellent collaborative opportunities for academia. Living collections also provide specimens and biological samples for a range of academic investigation, such as DNA analysis for comparative genetic purposes or reference, forensic evaluation, pathological reference, parasitology, studies in comparative anatomy or physiology, and genome banking.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 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".