{"id":"W2993730198","doi":"10.1515/bot-2018-0080","title":"Calculating macroalgal height and biomass using bathymetric LiDAR and a comparison with surface area derived from satellite data in Nova Scotia, Canada","year":2019,"lang":"en","type":"article","venue":"Botanica Marina","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadian Seaplants (Canada); Nova Scotia Community College","funders":"","keywords":"Lidar; Bathymetry; Intertidal zone; Environmental science; Remote sensing; Satellite; Satellite imagery; Biomass (ecology); Nova scotia; Oceanography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003968774,0.0002713126,0.0002144236,0.001396219,0.0009883497,0.0007272284,0.0005241205,0.0001599142,0.0008814188],"category_scores_gemma":[0.001000971,0.0002280866,0.0002637025,0.001736722,0.0002711937,0.0001854567,0.0004048507,0.0001653589,0.0002503588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009309234,"about_ca_system_score_gemma":0.008955751,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9915785,"about_ca_topic_score_gemma":0.9951609,"domain_scores_codex":[0.9996809,0.00002247987,0.00002028729,0.00006147435,0.0001519665,0.00006299215],"domain_scores_gemma":[0.9986506,0.00007575583,0.00009641718,0.00003207092,0.001019322,0.0001259218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001541792,0.00004099696,0.9611571,0.00008637301,0.00005507839,0.0002484715,0.0005636427,0.003149394,0.006180692,0.0001273003,0.000923108,0.02731363],"study_design_scores_gemma":[0.00001060835,0.00001956533,0.9922214,0.00002955891,0.00001208405,0.00004437103,0.0007128112,0.005004639,0.0006918159,0.00001752358,0.001226368,0.000009212441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930791,0.0002878369,0.0007030154,0.00005518047,0.000009088473,0.00005577871,0.002361477,0.0000450479,0.003403425],"genre_scores_gemma":[0.9952277,0.0001737173,0.001764406,0.00003212522,0.000001973857,0.00002230909,0.001322089,0.000009294068,0.001446361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009309234,"threshold_uncertainty_score":0.06754351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02361715568684286,"score_gpt":0.238791596358281,"score_spread":0.2151744406714381,"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."}}