{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001583995,0.001290875,0.001132763,0.001593722,0.001252369,0.003994598,0.002233316,0.001007241,0.01004808],"category_scores_gemma":[0.002686236,0.001052226,0.001169923,0.001059448,0.0008660149,0.001789605,0.003258921,0.00165772,0.006996345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001608817,"about_ca_system_score_gemma":0.003418804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01520509,"about_ca_topic_score_gemma":0.01192091,"domain_scores_codex":[0.9990278,0.0001354138,0.00009334626,0.0003244103,0.000307503,0.0001114129],"domain_scores_gemma":[0.9985399,0.0003096686,0.00009642506,0.0004755219,0.0003274448,0.0002510446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005661183,0.0004327883,0.005470934,0.000550881,0.0001413974,0.001326395,0.001330653,0.0705055,0.0385722,0.02687614,0.03780524,0.8164219],"study_design_scores_gemma":[0.0002374033,0.0002686912,0.007376979,0.0003447297,0.00008669517,0.0009352628,0.001046802,0.6567923,0.03972705,0.08990737,0.2029858,0.0002909129],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007470722,0.000385523,0.9432498,0.0007191362,0.00009839601,0.001030657,0.001008844,0.03402062,0.01201633],"genre_scores_gemma":[0.1121715,0.0007726436,0.8717583,0.0002847773,0.00007170311,0.0006748961,0.00315296,0.001280872,0.00983233],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01520509,"threshold_uncertainty_score":0.03361422,"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."}}