ASPECTS OF BEE BIODIVERSITY, CROP POLLINATION, AND CONSERVATION IN CANADA
Bibliographic record
Abstract
The risks to pollinator biodiversity in Canada are examined through a generalised model with inputs on environmental sensitivity, pressure indices, and societal response as they relate to the agriculture/environment interface. About 3,500 species of bees occur in America north of Mexico. Few genera are found in the USA and Canada that do not also occur in Mexico, however there are far fewer species in Canada. Canada has focused on the development on a few non-Apis species as well as the European honey bee as managed pollinators for specific crops with success for the alfalfa leafcutter bee, Megachile rotundata. The economic value of bee -affected pollination in Canada is great. Proposals for habitat management programs have resulted in little positive action, especially in agricultural systems. Nevertheless, Canadian society has responded to prot ect the environment and biodiversity. Over 3500 publicly owned protected areas and 550 private areas are recognised across the country. About 8% of the Canadian land base is protected through legislative programs. Numerous factors have influenced pollinator biodiversity and pollination including agriculture (cropland, pasture, irrigation, pesticides), forestry, urbanisation, access (road, rail, airports), utilities, extraction sites (mines, oil/gas), and pollution. Biotic factors of parasites, predators and diseases have also played an more natural role in regulating pollinator biodiversity. All these factors are influenced by human beings and may have long-term, negative consequences resulting in shortages of pollinator populations reserved for crop pollination. In reality, these pressures play a minor role in regulating/de creasing the density/diversity of pollinators compared to environmental factors (weather conditions, availability of nesting sites, food sources).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".