{"id":"W3190473853","doi":"10.1080/20964471.2021.1946290","title":"Capturing the value of biosurveillance “big data” through natural capital accounting","year":2021,"lang":"en","type":"article","venue":"Big Earth Data","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Guelph; University of Victoria","funders":"","keywords":"Biodiversity; Natural capital; Big data; Underpinning; Environmental resource management; Pace; Scale (ratio); Business; Ecosystem services; Ecosystem; Ecology; Economics; Geography; Computer science; Biology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003177325,0.0001014105,0.0001169791,0.000005478555,0.0001424385,0.00007239574,0.001286949,0.00003541269,0.003387437],"category_scores_gemma":[0.0002369289,0.00007486824,0.00002425378,0.0002391181,0.0001803304,0.0003910382,0.003171961,0.0001226845,0.0003883458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002955288,"about_ca_system_score_gemma":0.00002118708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001125135,"about_ca_topic_score_gemma":0.005255529,"domain_scores_codex":[0.9987617,0.00005173951,0.0001809087,0.0004274603,0.0003349965,0.000243183],"domain_scores_gemma":[0.997889,0.00006321873,0.00009260389,0.001911878,0.00001349026,0.0000298421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009099329,0.0006714239,0.5036729,0.0002517313,0.0002860141,0.000233713,0.004065071,0.0001278046,0.1099307,0.005685564,0.2204021,0.1545819],"study_design_scores_gemma":[0.0003068303,0.00000729928,0.641254,0.00002008882,0.00001637939,0.00003697136,0.002507936,0.0006792872,0.005354108,0.00004538941,0.3495634,0.0002082494],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840258,0.001161919,0.00007973648,0.001073235,0.0009630056,0.00009310927,0.004603823,0.00003195781,0.007967434],"genre_scores_gemma":[0.9950261,0.0002179352,0.0001581851,0.0003551946,0.0001606367,0.000001158068,0.003831505,0.00000786523,0.0002413903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1543737,"threshold_uncertainty_score":0.9975236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1144254386284098,"score_gpt":0.278752375489586,"score_spread":0.1643269368611762,"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."}}