Field data collected for "Estimating the ecological drivers of insect abundance when detection is imperfect"
Bibliographic record
Abstract
##--------------------------------------------------------------------------## DATA COLLECTION OVERVIEW We collected mark-recapture data for 8 species of wild bees from 10 field sites in summer 2022 and 2023. See the readme file "README.txt" for more info about how we analyzed the data. We collected mark-recapture data for wild bees, using a POSCA paint pen to apply marks (data specific but not individual bee specific marks). This allowed us to determine whether we previously encountered bees on our second and third repeat surveys to a site. We analyzed these data with multinomial N-mixture model. Alternatively, we ignored whether bees were marked and simply recorded the total number of marked / unmarked bees that were detected (a simple count). We analyzed these data with binomial N-mixture model or with a GLMM. The data were collected in 45 minute survey rounds of active survey time, using a sweep net and pausing the timer for handling bees caught in the net. All data were collected by J. Ulrich, with a second rotating observer following and helping to spot bees. The sites were an area of 100m x 100m, all in urban parks in Vancouver, Canada. Half of the sites were conventionally managed. For the other half of the sites, mowing was reduced and wildflower seeds were added. In https://doi.org/10.1111/ele.70037 we showed that this increased flowering species richness, and also increased vegetation height. All sites were surveyed 3 times per season, in summer 2022 and again in summer 2023. See the METADATA.txt file for more information on the dataset columns.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".