Data and code supporting 'Strengths and limitations of using participatory science data to characterize a wildlife mass mortality event'
Bibliographic record
Abstract
About this repositoryCitation:This repository can be cited as: Taylor, L.U., Barychka, T., McKeon, S., Bartolotta, N., Avery-Gomm, S. 2024. Strengths and limitations of using participatory science data to characterize a wildlife mass mortality event. FigShare. 10.6084/m9.figshare.25499647 This repository contains all data and code necessary to generate manuscript results and figures associated with the following peer-reviewed publication:Taylor, L.U., Barychka, T., McKeon, S., Bartolotta, N., Avery-Gomm, S. 2024. Strengths and limitations of using participatory science data to characterize a wildlife mass mortality event. Ecosphere.10.1002/ecs2.70051 Instructions:Data was sourced from Avery-Gomm et al. (2024), "Wild bird mass mortalities in eastern Canada associated with the Highly Pathogenic Avian Influenza A(H5N1) virus, 2022." While the data is included in this repository to facilitate replication of our analyses, those wishing to obtain the original HPAI mortality dataset from Avery-Gomm et al., (2024) should download it directly from their Figshare repository, not from this repository.To generate the manuscript results and figures, execute the analysis.r script. Figures and output files will be saved to the repository working directory. Supplementary comparisons with 2023 iNaturalist data (not presented in the manuscript) can also be generated by executing supplementary_analysis_2023_comparison.rData (in the Data/ directory):ne_50m_ocean (shapefiles for global coastline from Natural Earth Repository)Avery-Gomm_2024_DataS1.xlsx (ScenarioB_1day_1km dataset from [Avery-Gomm et al. 2024] obtained from DOI: 10.6084/m9.figshare.24856869)Clements-v2023-October-2023.csv (Clements/eBird avian taxonomy from Clements et al. 2023)iNaturalist_2022_observations-287490.csv (2022 iNaturalist mortality records from iNaturalist Project: HPAI | Dead birds in Eastern Canada during the 2022 HPAI outbreak, queried 2023-01-06)iNaturalist_2023_observations-407982.csv (2023 iNaturalist mortality records from iNaturalist Project: HPAI | Dead birds in Eastern Canada 2023 comparison, used in supplementary comparisons not presented in the manuscript, queried 2024-03-05)iNaturalist_2022_AllObservations.zip (All 2022 iNaturalist observations from the study area from iNaturalist Project: https://www.inaturalist.org/projects/eastern-canada-inat-reports, used for comparisons between iNaturalist mortality data and baseline reporting patterns)observation_blacklist.txt (The ID of "Needs ID" records from the iNaturalist source project that feature only photos of individual bleached bones deemed too old for our study)taxon_correction_dictionary.csv (manually assigned taxon dictionary to make comprehensive/iNaturalist records Clements-compliant)taxon_groupings_dictionary.csv (manually assigned taxon dictionary to assign general common name taxonomic groupings)gpw_v4_population_density_rev11_2020_15_min.tif (Study area population raster data from SEDAC 2020 Gridded Population of the World v4.11 dataset)Software requirementsScripts are written for R v4.3.2. See scripts and manuscript for packages and software citations.Required R packages can be installed in R with: install.packages(c("tidyverse", "readxl", "sf", "adehabitatHR", "ggtext", "patchwork", "terra"))
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.636 | 0.329 |
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".