{"id":"W6990605702","doi":"","title":"Drivers of Change in Haida Gwaii Kelp Forests: Combining Satellite Imagery with Historical Data to Understand Spatial and Temporal Variability","year":2022,"lang":"en","type":"dissertation","venue":"UVic’s Research and Learning Repository (University of Victoria)","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kelp; Kelp forest; Archipelago; Satellite imagery; Ecosystem; Climate change; Macrocystis pyrifera; Habitat","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":[0.0002654899,0.0001918437,0.0001653799,0.001521366,0.0002785136,0.0008346086,0.0002645903,0.0001995881,0.0004914224],"category_scores_gemma":[0.0007743498,0.0001733685,0.0001961097,0.002459922,0.0002338565,0.0005464462,0.0004113806,0.0004358495,0.0001153174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121778,"about_ca_system_score_gemma":0.0008047287,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2166865,"about_ca_topic_score_gemma":0.3752732,"domain_scores_codex":[0.9998553,0.00001018177,0.00001044397,0.00004297174,0.0000425705,0.00003850486],"domain_scores_gemma":[0.9994621,0.00008423965,0.0002186676,0.00002911658,0.0001330878,0.00007291273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000134752,0.00002199294,0.988211,0.00002462006,0.00004451442,0.00009304556,0.0005627177,0.0005167259,0.0006349107,0.00008763692,0.0005344643,0.009254839],"study_design_scores_gemma":[6.056922e-7,0.000003717132,0.9971847,0.00001111406,0.00001079734,0.00002873195,0.0006944372,0.001392484,0.00005292246,0.00002466783,0.0005920196,0.00000376736],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965118,0.0002115199,0.0001849012,0.0001269568,0.000008367314,0.0000139607,0.001353046,0.00001275248,0.00157681],"genre_scores_gemma":[0.9975322,0.0002252422,0.0003905607,0.00002234881,0.000007249546,0.00001618628,0.001406891,0.000005722019,0.0003936163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7833135,"threshold_uncertainty_score":0.4308504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05487093478965265,"score_gpt":0.2651356067958176,"score_spread":0.2102646720061649,"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."}}