{"id":"W2995849093","doi":"10.1101/2019.12.11.873182","title":"A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Landmark; Morphometrics; Artificial intelligence; Image registration; Pattern recognition (psychology); Computer science; Computer vision; Image (mathematics); Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001410991,0.0004472921,0.000637379,0.001259382,0.000237915,0.0003323293,0.0002955458,0.0007047144,0.000009004307],"category_scores_gemma":[0.004170441,0.0004423003,0.0001377492,0.002270546,0.00003120631,0.0001102927,0.0002913219,0.0006517337,0.00001937786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002455864,"about_ca_system_score_gemma":0.00009986928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002698184,"about_ca_topic_score_gemma":9.13991e-7,"domain_scores_codex":[0.9974538,0.0001446657,0.0005995173,0.0009708995,0.0003708359,0.0004602825],"domain_scores_gemma":[0.9974235,0.0005444558,0.0005857747,0.0007162927,0.0005046782,0.0002253477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009262465,0.005571433,0.05110335,0.02496972,0.002918878,0.00007344805,0.0001675936,0.02232555,0.8156648,0.05814404,0.01746862,0.0006662972],"study_design_scores_gemma":[0.005117741,0.001377923,0.3432937,0.0008190561,0.001403637,5.162331e-7,0.00005045172,0.5475423,0.06885928,0.0001751078,0.02608199,0.005278234],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3245632,0.0003074967,0.6720603,0.00003776527,0.0004100724,0.001665301,0.00007589593,0.0008359862,0.00004397023],"genre_scores_gemma":[0.8248092,0.00007254222,0.1743597,0.00004769839,0.0001939986,0.0004057639,0.000001463453,0.00008911825,0.00002052139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7468055,"threshold_uncertainty_score":0.9998029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158057807235605,"score_gpt":0.2523685422227536,"score_spread":0.2207879641503976,"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."}}