{"id":"W4417476250","doi":"10.36788/sah.v9i2.179","title":"Uso de Tracker para modelar matemáticamente fenómenos cinemáticos en secundaria","year":2025,"lang":"","type":"article","venue":"SAHUARUS REVISTA ELECTRÓNICA DE MATEMÁTICAS ISSN 2448-5365","topic":"Multidisciplinary Science and Engineering Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Noise (video); Statistical analysis; Background subtraction; Selection (genetic algorithm)","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","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.01856392,0.001938549,0.002835362,0.002204523,0.002169508,0.00634576,0.006935068,0.001382624,0.005892927],"category_scores_gemma":[0.00679979,0.001773437,0.001138175,0.007290804,0.001511258,0.001565126,0.00200485,0.003382519,0.003811946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002587779,"about_ca_system_score_gemma":0.005306227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002416405,"about_ca_topic_score_gemma":0.000028535,"domain_scores_codex":[0.9782664,0.002668741,0.004494509,0.003918955,0.004384335,0.00626703],"domain_scores_gemma":[0.9866269,0.004491513,0.0007638949,0.004898001,0.001088013,0.002131702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006252345,0.007392829,0.02760475,0.008340213,0.003842061,0.004606799,0.01630623,0.03133707,0.2009912,0.3896676,0.1681662,0.1354927],"study_design_scores_gemma":[0.004866905,0.001956347,0.02125655,0.003829107,0.001158597,0.0007016181,0.003653231,0.6385546,0.01087173,0.04333908,0.2654616,0.004350638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8057058,0.02621567,0.08533986,0.02674823,0.001806595,0.005953724,0.0004911015,0.0009810519,0.0467579],"genre_scores_gemma":[0.9620199,0.004322425,0.006943425,0.001059919,0.000295883,0.0003381389,0.00004419888,0.0002107545,0.02476536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6072175,"threshold_uncertainty_score":0.9999138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03824624710243533,"score_gpt":0.4158695493639234,"score_spread":0.377623302261488,"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."}}