{"id":"W2215613488","doi":"10.1016/j.bpj.2015.10.040","title":"The Impact of Collagen Fibril Polarity on Second Harmonic Generation Microscopy","year":2015,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; University of Ottawa; Institut National de la Recherche Scientifique","funders":"Versus Arthritis","keywords":"Second-harmonic generation; Femtosecond; Fibril; SIGNAL (programming language); Polarization (electrochemistry); Polarity (international relations); Microscopy; Second-harmonic imaging microscopy; Optics; Surface second harmonic generation; Materials science; Phase (matter); Collagen fibril; Chemistry; Biophysics; Physics; Laser; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0003335774,0.00009548468,0.000125944,0.00003082543,0.000252074,0.0002735805,0.000147277,0.00004891956,0.0003119015],"category_scores_gemma":[0.00006214732,0.00005928539,0.0001128553,0.0001230037,0.00006083907,0.0002119335,0.00002327115,0.00008596764,0.0001338876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001574387,"about_ca_system_score_gemma":0.0002351759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002132357,"about_ca_topic_score_gemma":0.000006147617,"domain_scores_codex":[0.9990736,0.0001370037,0.0002443739,0.0001126837,0.0002665798,0.0001657452],"domain_scores_gemma":[0.9992267,0.00003758102,0.0002370906,0.0001444317,0.0001990282,0.0001551175],"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":[0.0001351499,0.00008067412,0.00007539632,0.000001346168,0.000009148822,0.000002185343,0.00009944059,0.00002663969,0.9947846,0.00006150683,0.004357231,0.0003666671],"study_design_scores_gemma":[0.0003299928,0.0003683746,0.01037782,0.000006078252,0.000007241035,0.00003605591,0.00003281729,0.0006343794,0.9865998,0.00007178861,0.001460265,0.00007538773],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985194,0.00001790705,0.0002337438,0.0002342794,0.0006485252,0.00008032309,0.00003482511,0.0000121099,0.0002189398],"genre_scores_gemma":[0.9987161,0.00001265899,0.00007066989,0.00004748043,0.0008150248,0.00000158722,0.000006595528,0.000008159781,0.0003216796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01030243,"threshold_uncertainty_score":0.3415102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04010932310013881,"score_gpt":0.316350897663902,"score_spread":0.2762415745637631,"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."}}