Data and code supporting "Seabird and sea duck mortalities were lower during the second breeding season in eastern Canada following the introduction of Highly Pathogenic Avian Influenza A H5Nx viruses"
Bibliographic record
Abstract
About this repositoryCitation:This repository can be cited as:Cormier, T., Barychka, T., Beaumont, M., Burt, T. B., English, M. D., Giacinti, J. A., Giroux, J-F., Guillemette, M., Hargan, K. E., Jones, M., Lair, S., Lang, A. S., Lepage, C., Montevecchi W. A., Rahman, I., Rail, J-F., Robertson, G. J., Ronconi, R. A., Seyer, Y., Taylor, L. U., Ward, C. R. E., Wight, J., Wilhelm, S. I., Avery-Gomm, S. 2024. Data and Code supporting "Seabird and sea duck mortalities were lower during the second breeding season in eastern Canada following the introduction of Highly Pathogenic Avian Influenza A H5Nx viruses". FigShare. https://doi.org/10.6084/m9.figshare.25492693 This repository contains all data and code necessary to generate manuscript results associated with the following peer-reviewed publication: Cormier, T., Barychka, T., Beaumont, M., Burt, T. B., English, M. D., Giacinti, J. A., Giroux, J-F., Guillemette, M., Hargan, K. E., Jones, M., Lair, S., Lang, A. S., Lepage, C., Montevecchi W. A., Rahman, I., Rail, J-F., Robertson, G. J., Ronconi, R. A., Seyer, Y., Taylor, L. U., Ward, C. R. E., Wight, J., Wilhelm, S. I., Avery-Gomm, S. 2024. Seabird and sea duck mortalities were lower during the second breeding season in eastern Canada following the introduction of Highly Pathogenic Avian Influenza A H5Nx viruses. Bird Study, Vol. 71, No. 4. https://doi.org/10.1080/00063657.2024.2415161 InstructionsDataThis repository contains the complete collated dataset of reported wild bird mortalities in eastern Canada for October 1, 2022 to September 30, 2023 with anonymized personal information. See the manuscript above for details. Metadata describing the data is provided in Sheet: Metadata. Instructions for how to use the dataset are provided in Instructions for DataS1.txt Data S1. Reported mortalities and morbidities in Eastern Canada.xlsxInstructions for DataS1.txtPlease note: All data in DataS1 from April 1 to September 2022 were obtained from the Complete_Mortality_Dataset published by Avery-Gomm et al., (2024) and accessed from the data repository where that study published their data: https10.6084/m9.figshare.24856869.This repository also contains three .xlsx files with information on colony surveys from 2023 for Common Eiders (Data S2), Northern Gannets (Data S3) and Common Murres (Data S4). Also included in Data S2-4 are the best available information on recent breeding pair numbers and locations for all known colonies in eastern Canada, for each species, but this should not be construed or used as the official records on colony locations for Common Eiders or Common Mures. References are provided in Sheet: References. Metadata describing the data is provided in Sheet: Metadata. Data S2. American Common Eider (ACOEI) colony survey information.xlsxData S3. Northern Gannet (NOGA) colony survey information.xlsxData S4. Common Murre (COMU) colony survey information.xlsxCodeThe code required to reproduce the double count analysis is in Scenario_Analysis.Rmd.Scenario_Analysis.Rmd (input files are Data S1.csv and SpeciesAlphaCode_KEY.csv)Please note: This code was adapted from the Scenario_Analysis.Rmd provided by Avery-Gomm et al., (2024), accessed from the data repository where that study published their data: https10.6084/m9.figshare.24856869. Software RequirementsScripts are written for R version 4.2.2 (2022-10-31 ucrt). See scripts and the manuscript for packages and software citations.Required R packages can be installed in R with: install.packages(c("tidyverse", "dplyr", "data.table", "fossil", "DataCombine", "janitor", "here", "formattable", "openxlsx"]] Terms of Use: CC-BYYou are free to:Share — copy and redistribute the material in any medium or format for any purpose, even commercially.Adapt — remix, transform, and build upon the material for any purpose, even commercially.The licensor cannot revoke these freedoms as long as you follow the license terms.Under the following terms:Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.292 | 0.113 |
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".