{"id":"W7090880712","doi":"10.21966/9cbv-ra30","title":"Time series of surface kelp canopy area derived from remotely piloted aerial systems (RPAS, or drone) surveys, Central Coast, British Columbia","year":2015,"lang":"en","type":"dataset","venue":"Hakai Institute","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kelp; Canopy; Kelp forest; Habitat; Polygon (computer graphics); Tree canopy","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":[0.0001247641,0.0003481159,0.0002250189,0.001739485,0.0006473763,0.0008241114,0.0004762411,0.0001771313,0.005736335],"category_scores_gemma":[0.0008720123,0.0002039057,0.0001401694,0.003743343,0.0001518642,0.0002563586,0.0003044539,0.0003993756,0.001635328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00505495,"about_ca_system_score_gemma":0.004660289,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9800512,"about_ca_topic_score_gemma":0.9926304,"domain_scores_codex":[0.999818,0.000006351893,0.00001454275,0.00004681433,0.00007519075,0.00003907986],"domain_scores_gemma":[0.9984124,0.00007677347,0.000133408,0.00006382231,0.001170806,0.0001428428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002570834,0.0001240518,0.728752,0.000423113,0.0001842254,0.0004187704,0.0006566417,0.004967187,0.003418009,0.0003699572,0.197774,0.0626549],"study_design_scores_gemma":[0.0000118017,0.000009470304,0.9770772,0.00006218229,0.00001860897,0.00004378603,0.0006592711,0.002000851,0.0003536727,0.00002516128,0.01971697,0.0000211056],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.4154551,0.000442196,0.0008190128,0.0002072004,0.00005387523,0.0001016191,0.5661044,0.0004715572,0.01634496],"genre_scores_gemma":[0.5528578,0.0009154663,0.001981489,0.0001226915,0.00001704503,0.0002038342,0.4239246,0.0001069744,0.01987016],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01994884,"threshold_uncertainty_score":0.04013258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819186771826519,"score_gpt":0.2010303233976601,"score_spread":0.1728384556793949,"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."}}