{"id":"W4391445471","doi":"10.1101/2024.01.30.576574","title":"The collective application of shorebird tracking data to conservation","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Alberta; Trent University; Université de Moncton; McGill University; Université du Québec à Rimouski; Birds Canada; Carleton University; Environment and Climate Change Canada","funders":"U.S. Fish and Wildlife Service; Knobloch Family Foundation; ConocoPhillips","keywords":"Collective action; General partnership; Conservation science; Geography; Citizen science; Political science; Ecology; Biology; Biodiversity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0160437,0.0003214696,0.0003006227,0.002617986,0.00163366,0.003966825,0.0007621392,0.0009364492,0.002879462],"category_scores_gemma":[0.02994046,0.0002506332,0.0003418687,0.002294464,0.0014541,0.00190523,0.005114348,0.001135336,0.0006009718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009286533,"about_ca_system_score_gemma":0.001844975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004731806,"about_ca_topic_score_gemma":0.005792218,"domain_scores_codex":[0.9927585,0.004390646,0.000243605,0.00102123,0.001350885,0.0002351019],"domain_scores_gemma":[0.9746353,0.01320818,0.00238631,0.006038344,0.002683554,0.001048356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000195752,0.0003268393,0.2656301,0.0006196278,0.0003823231,0.0009098102,0.01870549,0.01394926,0.01551101,0.01962851,0.0372528,0.6268885],"study_design_scores_gemma":[0.0001845436,0.000749107,0.2757057,0.00132332,0.0004105895,0.0008861058,0.04885696,0.1043937,0.01923849,0.08691379,0.4610701,0.0002675908],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7484117,0.002132312,0.1547601,0.02313681,0.001511952,0.0007385366,0.003242528,0.00176941,0.06429652],"genre_scores_gemma":[0.8891342,0.0007247719,0.1036072,0.0006093386,0.0003888496,0.0002372008,0.001253981,0.0001730844,0.003871268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0160437,"threshold_uncertainty_score":0.08484817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567629927159185,"score_gpt":0.2619425763861062,"score_spread":0.2162662771145144,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}