{"id":"W4393711154","doi":"10.5281/zenodo.10509983","title":"Linked collectors and determiners for: Canadian Museum of Nature - National Biodiversity Cryobank of Canada.","year":2024,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biodiversity; Geography; Forestry; 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":[0.001628418,0.001728317,0.001513812,0.006515134,0.003265138,0.003996063,0.003973392,0.001342231,0.1156517],"category_scores_gemma":[0.01010661,0.001241408,0.0008597804,0.01601857,0.0009000135,0.001683197,0.002715312,0.002919625,0.07484618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01747511,"about_ca_system_score_gemma":0.03720342,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.879233,"about_ca_topic_score_gemma":0.9430417,"domain_scores_codex":[0.9980893,0.00009798938,0.0001353408,0.0004324105,0.0007790744,0.0004659621],"domain_scores_gemma":[0.9903983,0.0008415069,0.0005599014,0.001360712,0.005668477,0.001171161],"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":[0.00001069493,0.000003315277,0.0005264938,0.0001164219,0.000008127214,0.000005272088,0.00002326478,0.00005915412,0.00002748895,0.0004332536,0.9975661,0.00122045],"study_design_scores_gemma":[0.00004001508,0.000001783616,0.004679299,0.0001869604,0.00001235152,0.00001213281,0.00009660014,0.0001028055,0.0001726394,0.0008406572,0.9938279,0.00002691625],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004111335,0.00002324872,0.00004981058,0.00003783182,0.00001449167,0.00001015356,0.9986007,0.0001498869,0.00107286],"genre_scores_gemma":[0.0003636931,0.00005097341,0.0004176522,0.00004275245,0.000004500557,0.00008167642,0.9968069,0.0001631857,0.002068763],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.120767,"threshold_uncertainty_score":0.3868935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0272494603079504,"score_gpt":0.2662817152433519,"score_spread":0.2390322549354015,"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."}}