{"id":"W6944841561","doi":"10.21966/cz48-d388","title":"100 Islands Research Program Terrestrial Vegetation Data - BC Central Coast - 2015, 2016, 2017","year":2020,"lang":"en","type":"dataset","venue":"Hakai Institute","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Species richness; Biogeography; Metadata; Vegetation (pathology); Environmental data; Insular biogeography; Raw data; Biodiversity; Submarine pipeline","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002488823,0.001097492,0.001221152,0.0009414696,0.0008788712,0.001404651,0.006405112,0.001146324,0.0002960248],"category_scores_gemma":[0.002263713,0.001071683,0.0002216604,0.001440108,0.001474217,0.002513121,0.00363142,0.00385364,0.07599682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005885822,"about_ca_system_score_gemma":0.005041176,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008776323,"about_ca_topic_score_gemma":0.02075587,"domain_scores_codex":[0.9894862,0.0009523196,0.001307124,0.002642109,0.003266298,0.00234593],"domain_scores_gemma":[0.9918911,0.00018737,0.0007527864,0.005829886,0.0003335737,0.001005319],"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":[0.000854491,0.0007409213,0.00001828077,0.0004294549,0.0003576039,0.001090521,0.0001017334,0.00003496651,0.00003739525,0.00001797026,0.9935712,0.002745424],"study_design_scores_gemma":[0.003197795,0.000471831,0.0001885567,0.000787212,0.0003835317,0.00006713387,0.0000256298,0.0007133488,0.00001411517,0.00003994622,0.9930532,0.001057679],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002651909,0.0005890916,0.00002348494,0.0005091389,0.008192511,0.003623384,0.985471,0.0005441898,0.001020691],"genre_scores_gemma":[0.0001545744,0.001029682,0.0008110935,0.00008391077,0.009212672,0.0004026523,0.9876292,0.0002085135,0.0004677362],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0757008,"threshold_uncertainty_score":0.999632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2348943017256823,"score_gpt":0.4438858954431936,"score_spread":0.2089915937175113,"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."}}