{"id":"W1905352268","doi":"10.1038/ncomms9216","title":"Magnetic fingerprint of individual Fe4 molecular magnets under compression by a scanning tunnelling microscope","year":2015,"lang":"en","type":"article","venue":"Nature Communications","topic":"Quantum and electron transport phenomena","field":"Physics and Astronomy","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dell’Istruzione, dell’Università e della Ricerca; Alexander von Humboldt-Stiftung","keywords":"Fingerprint (computing); Microscope; Magnet; Quantum tunnelling; Molecular magnets; Compression (physics); Materials science; Scanning tunneling microscope; Nanotechnology; Computer science; Condensed matter physics; Nuclear magnetic resonance; Physics; Optoelectronics; Magnetization; Optics; Computer vision; Composite material; Magnetic field; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0001915486,0.0001386285,0.0001769118,0.00006010951,0.0001295722,0.0000359082,0.0008828098,0.0001023218,0.00005943839],"category_scores_gemma":[0.00000435,0.0001386648,0.0000654697,0.0002068254,0.0001059567,0.00005884655,0.0001711445,0.0007059784,0.000009088415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001938308,"about_ca_system_score_gemma":0.0000889687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009683864,"about_ca_topic_score_gemma":0.00001071622,"domain_scores_codex":[0.9991134,0.00008541151,0.0002514002,0.0001633612,0.0001707815,0.0002156703],"domain_scores_gemma":[0.9987485,0.0000635346,0.0001205001,0.0008434234,0.0001172437,0.0001067658],"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":[0.0001294986,0.003048336,0.06698951,0.00009877258,0.0005508474,0.000002360108,0.008696051,0.004703067,0.5515907,0.2906382,0.03073487,0.04281778],"study_design_scores_gemma":[0.0120966,0.001641755,0.05088196,0.001375829,0.001438732,0.00001224541,0.01182538,0.013397,0.4629295,0.1348257,0.3051902,0.004385158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9263004,0.04413781,0.0138651,0.001357479,0.0001237639,0.0003559497,0.0001554014,0.00006177593,0.0136423],"genre_scores_gemma":[0.9934786,0.00003797012,0.005806716,0.0001258333,0.00001795614,0.00002123016,0.0003950839,0.0000202839,0.00009630797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2744553,"threshold_uncertainty_score":0.5654587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01805107262323034,"score_gpt":0.277363118658316,"score_spread":0.2593120460350856,"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."}}