{"id":"W4396585523","doi":"10.1111/rec.14164","title":"Automated precise seeding with drones and artificial intelligence: a workflow","year":2024,"lang":"en","type":"article","venue":"Restoration Ecology","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"LifeWatch – Niclas Öberg Foundation; Universidad de Granada; Ministerio de Ciencia e Innovación; Agencia Estatal de Investigación; Canada Research Chairs; European Commission","keywords":"Seeding; Drone; Microsite; Computer science; Scale (ratio); Workflow; Automation; Artificial intelligence; Environmental science; Seedling; Engineering; Geography; Cartography; Biology; Database","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001167391,0.00005978214,0.00005967428,0.00003826856,0.0001135626,0.00006659182,0.00003648318,0.00005539046,0.0002061785],"category_scores_gemma":[0.00001410053,0.00004969033,0.00000872266,0.0002387491,0.0001264746,0.0001099868,0.00002141908,0.00006878628,0.000345405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007249505,"about_ca_system_score_gemma":0.00001530387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004242007,"about_ca_topic_score_gemma":0.0004841633,"domain_scores_codex":[0.9994884,0.00002762289,0.0001069289,0.0001967816,0.00007287904,0.0001073678],"domain_scores_gemma":[0.9997911,0.00005935414,0.00002098497,0.0000826972,0.00000553701,0.00004032105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009371326,0.0001692866,0.01123563,0.00004732107,0.00005828606,0.00005614766,0.006216789,0.04079945,0.05814102,0.01511183,0.01983232,0.8482382],"study_design_scores_gemma":[0.00004188257,0.0001895749,0.04224476,0.00003100502,0.00002914486,0.00009316874,0.0002686846,0.9266801,0.001897197,0.006066658,0.0222469,0.0002109501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776334,0.00005627522,0.01568067,0.002456859,0.0001658606,0.000217326,0.000001151577,0.0004650464,0.003323413],"genre_scores_gemma":[0.995159,0.00001447722,0.004383766,0.00004256481,0.00004784988,0.000006664224,0.000005925916,0.000007823775,0.0003319321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8858806,"threshold_uncertainty_score":0.4439597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01591138521013488,"score_gpt":0.2664987975518219,"score_spread":0.250587412341687,"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."}}