Characterizing insect communities within thin-soil environments
Bibliographic record
Abstract
Natural thin-soil environments are those which have little to no soil accumulation atop hard substrates. Many of these natural thin-soil environments, such as alvars, rocky lakeshores or glades, cliffs and cliff bluffs, and barrens, are found in the Great Lakes Region of North America. Due to their ubiquity and ecosystem services they provide, characterizing insects in sensitive environments such as these is important. This study monitored insects in nine thin-soil sites, within three regions, on a 630 km latitudinal gradient in the Southeastern Great Lakes Region of North America from June - August 2019. Over 22,000 insect specimens collected were identified to order or family, and bee specimens were identified to genus or species. We found that overall insect community composition and biodiversity characteristics were similar between the three regions examined. However, the central region had higher taxonomic richness than the southern region. Although unique bee taxa were observed in each region, diversity metrics and community composition of bees were similar among sites. This study provides taxonomic information about the insect, particularly bees, and plant communities in thin-soil environments in this region, which could support conservation and management efforts.
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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 | 0.001 |
| 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".