{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001712656,0.0005421126,0.0002819572,0.000325388,0.0002338748,0.0002437131,0.0002317874,0.0003757891,0.001128391],"category_scores_gemma":[0.0001275481,0.0001699192,0.000242505,0.0002253946,0.0001606121,0.0001807899,0.0001729762,0.0003481426,0.0002398437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004398712,"about_ca_system_score_gemma":0.0001656668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003166731,"about_ca_topic_score_gemma":0.003364898,"domain_scores_codex":[0.9998875,0.00001834563,0.0000108448,0.00003485378,0.0000302363,0.0000181562],"domain_scores_gemma":[0.9998981,0.00002430429,0.00002132909,0.000007963063,0.00003489515,0.00001337016],"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":[0.00001873956,0.000006898132,0.0000868575,0.00002892643,0.000002320639,0.00001466487,0.00001346611,0.0000236269,0.9993893,0.00001384862,0.00002095361,0.000380509],"study_design_scores_gemma":[0.000002569736,0.00009749108,0.002391099,0.000005520755,0.00000872787,0.00003306939,0.00002421501,0.0005566687,0.9962464,0.00002226169,0.0006078561,0.000004073324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986562,0.001396938,0.008021181,0.0001041789,0.00003247043,0.00006755772,0.0007721949,0.0003534598,0.002690091],"genre_scores_gemma":[0.981914,0.00127677,0.01000925,0.0001063913,0.000008411615,0.0001870905,0.00122214,0.00009917833,0.005176805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003166731,"threshold_uncertainty_score":0.006296635,"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."}}