A data set for pollinator diversity and their interactions with plants in the Pacific Northwest
Bibliographic record
Abstract
Pollinator populations have declined substantially in recent years. The resulting loss in pollination services has both ecological and economic consequences, including reductions in plant diversity and crop production and lower food security. Data sets that identify pollinators and their plant hosts are of the utmost importance for the light can shed on the main causes of pollinator declines. Here we present a data set that contains 67,954 individual pollinator records. The data were collected across the Pacific Northwest, primarily focused in British Columbia (Canada), with 182 individual sites over 11 years, between 2005 and 2017. This data set comprises multiple studies that aimed to collect information on pollinator abundance, diversity, and their interactions with plants. Overall, the data set includes 937 morphospecies (of which 482 were identified to the species level) of pollinators across 105 families, including data for bees, wasps, butterflies, moths, and flies. We also present information on the interactions of these species, with 473 species of plants. The data set is being released for noncommercial use only. Credits should be given to this paper (i.e., proper citation).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".