{"id":"W4407685124","doi":"10.24124/2024/59603","title":"Machine learning based classification of early seral vegetation in cut-blocks in the interior of northern British Columbia","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Tree Root and Stability Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Seral community; Vegetation (pathology); Geography; Forestry; Artificial intelligence; Computer science; Machine learning; Ecology; Ecological succession; Medicine","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":[0.0002790124,0.0003688747,0.0002849879,0.001906445,0.00106762,0.001592996,0.0005675637,0.0003809055,0.00123558],"category_scores_gemma":[0.0009216628,0.00015088,0.0002113611,0.001455855,0.0004479781,0.0002699961,0.0003546045,0.000389764,0.000436608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00435412,"about_ca_system_score_gemma":0.002360784,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8949018,"about_ca_topic_score_gemma":0.9683509,"domain_scores_codex":[0.9997826,0.00001556989,0.00001282507,0.00005537892,0.00005392115,0.00007970344],"domain_scores_gemma":[0.9992872,0.0001182795,0.00004901298,0.00002767535,0.0004328993,0.00008500832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003069296,0.0002603844,0.7994515,0.0001775657,0.00008688993,0.0006567261,0.001063082,0.01702869,0.01188751,0.0002595494,0.008877132,0.1599441],"study_design_scores_gemma":[0.00001318371,0.00002762097,0.9107488,0.00007765124,0.00003042456,0.00007844932,0.00259417,0.08180535,0.00135408,0.00010268,0.003139428,0.00002806875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940636,0.0002707207,0.0008399157,0.0001337952,0.00001539382,0.00004866785,0.001349005,0.00008954564,0.003189301],"genre_scores_gemma":[0.991004,0.0002105608,0.002164093,0.00004808754,0.000004673906,0.00002239155,0.003048983,0.000013669,0.003483543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1050982,"threshold_uncertainty_score":0.2114342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009617406368931686,"score_gpt":0.2242392761253483,"score_spread":0.2146218697564166,"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."}}