{"id":"W4385664411","doi":"10.1111/cobi.14161","title":"Using social media records to inform conservation planning","year":2023,"lang":"en","type":"article","venue":"Conservation Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Koneen Säätiö; Ministerio de Ciencia e Innovación; University of Queensland; Deutsche Forschungsgemeinschaft","keywords":"Geography; Biodiversity conservation; Biodiversity; Distribution (mathematics); Global biodiversity; Citizen science; Political science; Humanities; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003177482,0.0003612718,0.0002049137,0.008719458,0.0007727307,0.002709979,0.0008060418,0.0005737544,0.008421824],"category_scores_gemma":[0.0234079,0.0002792343,0.0002049639,0.007257883,0.0005163997,0.003227239,0.001695479,0.0005666372,0.001271499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007566812,"about_ca_system_score_gemma":0.001036031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01882306,"about_ca_topic_score_gemma":0.03844924,"domain_scores_codex":[0.9968296,0.001662091,0.0003377984,0.000338829,0.0006463087,0.0001853621],"domain_scores_gemma":[0.9566933,0.02235691,0.0108425,0.003819646,0.005310301,0.000977346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001707784,0.0002090052,0.7142029,0.0008410002,0.0002903698,0.0003131608,0.004005143,0.004006297,0.001177352,0.004067953,0.02293931,0.2477767],"study_design_scores_gemma":[0.00004443365,0.0001717666,0.7786712,0.002669547,0.0003195874,0.0003338297,0.02562678,0.03652727,0.005480606,0.01298703,0.1369383,0.000229656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8137182,0.001803536,0.02016724,0.005971262,0.0004501514,0.000896821,0.05644001,0.0004728527,0.1000799],"genre_scores_gemma":[0.9686505,0.0007198401,0.01749354,0.0003299823,0.0001752711,0.0003350582,0.008811049,0.00003627087,0.003448606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01882306,"threshold_uncertainty_score":0.03742701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2083841745980196,"score_gpt":0.3567226924560827,"score_spread":0.1483385178580631,"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."}}