{"id":"W2046142554","doi":"10.1155/2010/763142","title":"Evaluation of Copper Oxide Nanoparticles Toxicity Using Chlorophyll <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mi>a</mml:mi></mml:math> Fluorescence Imaging in <i>Lemna gibba</i>","year":2010,"lang":"en","type":"article","venue":"Journal of Botany","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photosystem II; Photosynthesis; Chlorophyll fluorescence; Lemna gibba; Quenching (fluorescence); Copper oxide; Fluorescence; Chlorophyll; Toxicity; Copper; Materials science; Nuclear chemistry; Biology; Botany; Chemistry; Aquatic plant; Physics; Organic chemistry; Ecology; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003691707,0.0001334257,0.0001716623,0.00007723775,0.0001903163,0.0001472848,0.0003508632,0.0001004035,0.00009069622],"category_scores_gemma":[0.0006051405,0.000140052,0.0001507841,0.0002072358,0.0001919805,0.0005400927,0.0001152588,0.0002243824,0.00008869747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002751615,"about_ca_system_score_gemma":0.000309959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001114195,"about_ca_topic_score_gemma":0.00009477931,"domain_scores_codex":[0.9976701,0.0001465164,0.0007313729,0.0002230015,0.0009023648,0.000326674],"domain_scores_gemma":[0.9983575,0.0001949045,0.0007366771,0.0003587499,0.0002196045,0.0001325796],"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":[0.00005055084,0.0001331797,0.0001371055,0.00002020098,0.00001131019,0.00001485143,0.000222448,0.0004089382,0.8805926,0.117145,0.00005087439,0.001212967],"study_design_scores_gemma":[0.0003091804,0.00005076399,0.00148201,0.0001543627,0.0001063676,0.0001143082,0.0001894015,0.2073129,0.7895966,0.0005192466,0.0000581871,0.0001066169],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989435,0.0001797935,0.0001180607,0.0002748392,0.0002904655,0.00004653558,0.00001176001,0.00001557591,0.000119454],"genre_scores_gemma":[0.9942901,0.00003175284,0.005361342,0.00009877707,0.0001685668,0.00001877026,5.473759e-7,0.00002812385,0.000002031589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.206904,"threshold_uncertainty_score":0.5711156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02424403183330476,"score_gpt":0.2676316759082065,"score_spread":0.2433876440749017,"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."}}