{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217896,0.0001575208,0.0004098249,0.0003634714,0.0004492619,0.00003406901,0.000397791,0.0001685246,0.000276123],"category_scores_gemma":[0.0001072128,0.0001672189,0.0000270025,0.0003664249,0.0001875232,0.0002575578,0.0001976908,0.0009368892,0.000001298048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007551853,"about_ca_system_score_gemma":0.0002244833,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1959604,"about_ca_topic_score_gemma":0.1783074,"domain_scores_codex":[0.9977631,0.0006062502,0.0001938264,0.0005833977,0.0005407666,0.0003126444],"domain_scores_gemma":[0.9986331,0.00058052,0.0001807806,0.0002890412,0.0001276745,0.0001888299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002150772,0.00004927095,0.9703792,0.0003265745,0.00004929185,0.0001982942,0.006405698,0.00002068324,0.0000995525,0.00001883608,0.0001218587,0.02017997],"study_design_scores_gemma":[0.0007361843,0.002610154,0.9436798,0.0001621406,0.00003597656,0.00001684452,0.02287359,0.00124187,0.000006992485,0.00008136354,0.02826913,0.0002859719],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911517,0.0005458876,0.00002183725,0.00006987093,0.0002883494,0.0003358595,0.00008084433,0.00001475337,0.007490932],"genre_scores_gemma":[0.9937087,0.0003131528,0.0001649224,0.000002662154,0.00007325439,3.030461e-7,0.001711422,0.000005809904,0.004019796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02814727,"threshold_uncertainty_score":0.8366863,"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."}}