{"id":"W6963899795","doi":"10.21966/03pw-2190","title":"Time series of eelgrass (Zostera marina) meadow extent derived from drone surveys, Central Coast, British Columbia","year":2015,"lang":"en","type":"dataset","venue":"Hakai Institute","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Zostera marina; Geospatial analysis; Aerial photography; Aerial survey; Drone; Polygon (computer graphics); Estuary; Underwater; Seagrass","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":[0.0001305112,0.0003178092,0.0002189436,0.002258139,0.0006241404,0.0009880419,0.0004195485,0.0001380524,0.006824168],"category_scores_gemma":[0.0008204017,0.0002079307,0.0001614817,0.004024059,0.0001446068,0.0002202117,0.0003675974,0.0003374757,0.001674197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005149202,"about_ca_system_score_gemma":0.003975493,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9792671,"about_ca_topic_score_gemma":0.9933076,"domain_scores_codex":[0.9998197,0.000006636124,0.00001442306,0.00004214424,0.00007926742,0.00003781628],"domain_scores_gemma":[0.9987012,0.00005953708,0.0001097084,0.00005308544,0.0009524152,0.0001240877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002965177,0.00009686971,0.7649803,0.0003919751,0.0002180257,0.0003435846,0.0008729964,0.004103794,0.003722623,0.000396193,0.1580352,0.06654197],"study_design_scores_gemma":[0.000009572541,0.000008176598,0.9773999,0.00004768074,0.0000195199,0.00003540984,0.0005527132,0.001209554,0.0003320409,0.00001789341,0.02035019,0.00001732211],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.4694414,0.0004286793,0.0007657451,0.000168665,0.00003843571,0.0001078012,0.5086499,0.0005738524,0.01982545],"genre_scores_gemma":[0.5503926,0.001011049,0.002210308,0.000102428,0.000013428,0.0002143508,0.4185592,0.0001318911,0.02736483],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02073288,"threshold_uncertainty_score":0.0417099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210184652797306,"score_gpt":0.2323580948206148,"score_spread":0.2102562482926417,"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."}}