{"id":"W7036708053","doi":"","title":"CAMcal: A Program for Camera Calibration Using Checkerboard Patterns","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Phytochemistry Medicinal Plant Applications","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Calibration; Checkerboard; Camera resectioning; Bundle adjustment","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005954512,0.0000709142,0.0000821353,0.000002848429,0.0001042922,0.00002514847,0.00008096753,0.00004939878,0.0001928875],"category_scores_gemma":[0.00004179372,0.00002935574,0.00004028476,0.0001074759,0.00002071843,0.00004719198,0.000007409995,0.00004829188,0.000002751623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001779718,"about_ca_system_score_gemma":0.000009308905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002875357,"about_ca_topic_score_gemma":0.00004654527,"domain_scores_codex":[0.9994546,0.00001470823,0.0001062268,0.0001630582,0.00009348116,0.0001678908],"domain_scores_gemma":[0.999758,0.00005785465,0.00004430444,0.00003726476,0.00003310406,0.00006946974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005361123,0.00005747464,0.001560149,0.000008246513,0.000003730521,4.536458e-7,0.0000141305,0.000001577734,0.9807579,0.0004748203,0.0007031586,0.01641303],"study_design_scores_gemma":[0.0006069338,0.0004644236,0.01373674,0.00008233063,0.00007535573,0.00006994127,0.0006167513,0.01135308,0.7619509,0.01123133,0.1991442,0.0006679961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958938,0.000009638788,0.0005235188,0.0005901964,0.00003307012,0.0005097921,0.00006075792,0.00007453991,0.002304711],"genre_scores_gemma":[0.9961645,0.00000335674,0.003130946,0.0001317322,0.0001546416,0.0002103579,0.0001225255,7.641631e-7,0.00008118958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.218807,"threshold_uncertainty_score":0.2111983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03998071060080766,"score_gpt":0.2798365002561425,"score_spread":0.2398557896553348,"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."}}