Code and data for: Traits, threats, and popularity explain extinction risk of bird
Bibliographic record
Abstract
README for Supplementary MaterialsTitle of Paper: Traits, threats, and popularity explain extinction risk of birds globally Authors: Janaina Serrano, Lars Iversen, Laura Pollock DescriptionThis README file provides an overview of the dataset, columns, and code files included in the supplementary materials for the paper titled Traits, threats, and popularity explain extinction risk of birds globally. The dataset contains information on bird species, their traits, threats they face, habitat characteristics, and popularity metrics. The associated code files are provided to facilitate reproducibility and further exploration.Files in Supplementary Materials1. Dataset FileFilename: datFormat: CSVDataset DescriptionColumns in the DatasetColumn NameDescriptionspeciesScientific name of the bird species.threatenedBinary indicator of whether the species is classified as threatened (1/0).HabitatPrimary habitat type for the species.Order1Taxonomic order to which the species belongs.total_threatsTotal number of threats affecting the species.MigrationMigration status of the species (e.g., migratory or non-migratory).HabitatBreadthBreadth of habitat types the species can occupy (numerical value).Range.SizeGeographical range size of the species (in square kilometers).MassBody mass of the species (in grams).gbif_meanMean number of GBIF records indicating species' data availability.meanMean number of Google search hits for the species (popularity metric).pollutionImpact of pollution threats on the species.loggingImpact of logging threats on the species.invasiveImpact of invasive species threats on the species.agricultureImpact of agricultural activities on the species.climate_changeImpact of climate change threats on the species.huntingImpact of hunting threats on the species.Mass.logLog-transformed body mass.Range.Size.logLog-transformed range size.gbif_mean.logLog-transformed GBIF mean value.HabitatBreadth.logLog-transformed habitat breadth.google.logLog-transformed Google search popularity.Migration1Categorical indicator of migration type.Mass.log.csCentered and scaled log-transformed body mass.Range.Size.log.csCentered and scaled log-transformed range size.gbif_mean.log.csCentered and scaled log-transformed GBIF mean value.HabitatBreadth.log.csCentered and scaled log-transformed habitat breadth.google.log.csCentered and scaled log-transformed Google popularity. 2. Code FilesDescriptions of the provided code files:FilenameDescriptiondataprep_birdtraitsScript for preparing and cleaning the bird trait dataset. Includes processes like data wrangling, standardization of column names, log-transformations of variables, and preparation of final input data for analysis.model_figures_scriptScript to generate the main figures from the paper. This includes plotting extinction risk models, trait relationships, and visualizations of the species' threats and popularity metrics.model_evaluation_bivmapScript for model evaluation and bivariate mapping. Evaluates the predictive performance of models and creates spatial maps to visualize overlaps modeled extinction risk and observed conservation status of birds.gtrendsScript for collecting and processing Google Trends data related to species' popularity. Retrieves search hit data, processes it for analysis, and calculates summary metrics like mean hits and log-transformed values.GBIF_download Script for collecting and processing GBIF number of observations for birds globally from 2004-2021. Retrieves GBIF data, processes it for analysis, and calculates average species observations per year.Data Usage and CitationThe dataset is provided as supplementary material for the paper and can be used for academic purposes. If you use this dataset, please cite the paper as follows:Serrano J., Iversen L., Pollock L. (2025). Traits, threats, and popularity explain extinction risk of birds globally.Contact InformationFor questions about the dataset or paper, please contact:Janaina Serrano: janaina.serrano@mail.mcgill.caLars Iversen: lars.iversen@mcgill.caLaura Pollock: laura.pollock@mcgill.ca
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.335 | 0.222 |
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".