{"id":"W4235417955","doi":"10.24908/iqurcp.7884","title":"12. Solving a Maze Using DNA Computing","year":2017,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"DNA computing; Computer science; In silico; Supercomputer; Routing (electronic design automation); Scope (computer science); Parallel computing; Computation; Distributed computing; Algorithm; Theoretical computer science; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002835923,0.0004687837,0.0002754122,0.0003246515,0.000708712,0.001224797,0.0006547886,0.001104736,0.01178361],"category_scores_gemma":[0.001190057,0.0001989282,0.0005370422,0.0003692336,0.0007560808,0.001431786,0.000625597,0.0007240256,0.004807973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005809995,"about_ca_system_score_gemma":0.0009590869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001707526,"about_ca_topic_score_gemma":0.001375579,"domain_scores_codex":[0.9997757,0.00004729762,0.00001406067,0.00004070208,0.00009484334,0.00002731856],"domain_scores_gemma":[0.9997377,0.00007850069,0.00001906162,0.00004786949,0.00009261831,0.00002425857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001250366,0.0001546336,0.002196085,0.0006726304,0.00004844435,0.0003312259,0.0005734748,0.09997173,0.03094911,0.4944603,0.01881147,0.351706],"study_design_scores_gemma":[0.0001153462,0.0002603328,0.0009071031,0.0002898473,0.00004009985,0.0009313381,0.0003842768,0.3146774,0.05513887,0.2896874,0.337477,0.00009097446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01722691,0.0007513761,0.8891813,0.001971146,0.0003574546,0.0002027757,0.0001307316,0.001789702,0.08838869],"genre_scores_gemma":[0.1383896,0.001176288,0.8248468,0.0005327182,0.00007582049,0.0002528779,0.0002315176,0.0002389424,0.03425535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01178361,"threshold_uncertainty_score":0.03942007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1740694091953904,"score_gpt":0.4069518911771896,"score_spread":0.2328824819817992,"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."}}