{"id":"W6923371653","doi":"10.14288/1.0387711","title":"Index to the interim forest cover series and the forest inventory area reference system; Canada sheet 93 N/NW, N/SW","year":2019,"lang":"en","type":"other","venue":"Open Collections","topic":"","field":"","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":[0.0002627957,0.0008855893,0.0004869328,0.004188781,0.000990305,0.001284935,0.001041461,0.0001972888,0.2824058],"category_scores_gemma":[0.001648406,0.0003291834,0.0001517062,0.01320405,0.0002362122,0.0007058114,0.0005427822,0.0006245733,0.1854988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004141694,"about_ca_system_score_gemma":0.009035633,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7390869,"about_ca_topic_score_gemma":0.7726634,"domain_scores_codex":[0.9996699,0.00001613833,0.0000228452,0.00004564725,0.000191572,0.00005385962],"domain_scores_gemma":[0.9983535,0.00007683002,0.00007658408,0.0001033277,0.001263417,0.0001264094],"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":[0.00001397133,0.000007375289,0.0003576247,0.000082422,0.000001481768,0.000009379934,0.00003331881,0.00005065369,0.00004803842,0.0003902882,0.9772103,0.02179507],"study_design_scores_gemma":[0.00001034039,0.000004847958,0.01042276,0.00005936124,0.000002376674,0.00002030815,0.00008022206,0.0001069076,0.00008807863,0.0001896557,0.9890066,0.000008552753],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0008525361,0.0002963437,0.0004084393,0.0001577141,0.0001572877,0.0002440661,0.8425046,0.0008953061,0.1544837],"genre_scores_gemma":[0.006279329,0.0009651734,0.002185615,0.0001027587,0.000061554,0.0003244409,0.7119434,0.0008927505,0.277245],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2824058,"threshold_uncertainty_score":0.9447416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02578109799808014,"score_gpt":0.2438983059770823,"score_spread":0.2181172079790021,"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."}}