Floral resource foraging habits of solitary bees in habitat mosaics
Bibliographic record
Abstract
Floral resource foraging habits of solitary bees in habitat mosaicsBees require a variety of resources such as suitable nesting sites and floral resources (nectar & pollen).However, a single habitat type may not contain all of these resources.Habitat mosaics, areas that contain different habitat types with different spatially-separated resources, allow us to study the effects of these limitations.The purpose of this study was to determine the foraging distances of Osmia and Megachile solitary bees.This was done by looking at the relative abundance of Ericaceae pollen in fecal pellets and pollen provisions in the bees' nests.In the Ottawa region, plants of the Ericaceae family are only found within the Mer Bleue and other bogs.Therefore, we were able to use the relative abundance of Ericaceae pollen as a metric of the maximum foraging distance of the bees.This project was exploratory as well since we wanted to determine if the bees entered the bog at all to forage and if the Ericaceae pollen is detectable and measurable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".