{"id":"W6961229708","doi":"10.14288/1.0387595","title":"Index to the interim forest cover series and the forest inventory area reference system; Canada sheet 94 E/NW, E/SW","year":2019,"lang":"en","type":"other","venue":"Open Collections","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forest inventory; Forest cover; Index (typography); Scale (ratio); Mile; Cover (algebra); Interim","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":[0.000334032,0.0009430714,0.0005063376,0.005317681,0.0009229242,0.001418784,0.001054336,0.0002136805,0.2284309],"category_scores_gemma":[0.002240162,0.0003221173,0.0001586562,0.01538146,0.0002704142,0.0008044338,0.0005346952,0.0006733265,0.1631043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004572295,"about_ca_system_score_gemma":0.01003148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7033419,"about_ca_topic_score_gemma":0.6944105,"domain_scores_codex":[0.9995543,0.00002297013,0.00003380898,0.000058613,0.0002653881,0.0000648206],"domain_scores_gemma":[0.9976705,0.0001154049,0.0001214986,0.00015523,0.001789188,0.0001480833],"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":[0.00001460991,0.000007361502,0.0003471613,0.00007855864,0.000001688886,0.000008323857,0.00002833051,0.00006115662,0.00004715863,0.0004777187,0.9772783,0.02164961],"study_design_scores_gemma":[0.00001072961,0.000004942605,0.00871448,0.00005176638,0.000003011257,0.00001885305,0.00006836976,0.0001407717,0.00009912257,0.0002658564,0.9906136,0.000008431591],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0008158297,0.0003451218,0.0005996875,0.0002069701,0.0002047536,0.0002859655,0.8580144,0.001207468,0.1383197],"genre_scores_gemma":[0.006026939,0.001006534,0.002821378,0.000115755,0.00008059126,0.0003567245,0.7612814,0.001003507,0.2273072],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2966581,"threshold_uncertainty_score":0.7641774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08668827271064783,"score_gpt":0.3177425102426422,"score_spread":0.2310542375319944,"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."}}