{"id":"W3105060093","doi":"10.1111/1365-2656.13388","title":"Connecting the data landscape of long‐term ecological studies: The SPI‐Birds data hub","year":2020,"lang":"en","type":"article","venue":"Journal of Animal Ecology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Environment Research Council; Sight Research UK; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Norges Forskningsråd; Agence Nationale de la Recherche; British Ecological Society","keywords":"Metadata; Generality; Interoperability; Scale (ratio); Data integration; Ecology; Term (time); Distribution (mathematics); Geography; Data science; Computer science; Database; Biology; World Wide Web; Cartography","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.03720875,0.0007183254,0.001175223,0.01028788,0.001781545,0.007781162,0.004599663,0.001985535,0.01426536],"category_scores_gemma":[0.06033939,0.001076632,0.0009181879,0.01256561,0.001878907,0.01504889,0.01532588,0.002865325,0.009730111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002217338,"about_ca_system_score_gemma":0.009762311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006351814,"about_ca_topic_score_gemma":0.004822094,"domain_scores_codex":[0.9881506,0.003077274,0.002298521,0.002527938,0.003390804,0.0005549011],"domain_scores_gemma":[0.9000695,0.02386353,0.007208934,0.0394413,0.01472363,0.01469302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001543948,0.0004768043,0.08525608,0.002606959,0.0006398993,0.0008398397,0.005119456,0.00520088,0.008525033,0.1126338,0.4303559,0.3468014],"study_design_scores_gemma":[0.0002037276,0.0001958084,0.03456491,0.0009819967,0.0001427478,0.0004585555,0.001721133,0.008315175,0.005026001,0.05034848,0.8978052,0.0002362018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03661237,0.002906718,0.5557316,0.02011854,0.001140308,0.002805875,0.2336758,0.08836418,0.05864464],"genre_scores_gemma":[0.1511284,0.001925983,0.3961615,0.003925863,0.0007094472,0.002633442,0.4196333,0.01059025,0.01329185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9954003,"threshold_uncertainty_score":0.196781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2013491126932674,"score_gpt":0.3547633609570623,"score_spread":0.1534142482637949,"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."}}