Variable importance plot (mean decrease accuracy and mean decrease Gini). by Fernando Martinez-Taboada (8655132)

“…The higher the value of mean decrease accuracy or mean decrease Gini score, the higher the importance of the variable in the model.…”

Does Mean Platelet Volume Decrease in the presence of Coronary Artery Fistula? by Isa Sincer (7234577)

“…Several studies have shown the association between mean platelet volume (MPV) and cardiovascular diseases. …”

Comparison of mean differences in temperature decreases. by Hans-Jörg Gillmann (8460585)

“…<p>Patients are grouped according to receiving either dipyrone (blue; n = 341) or acetaminophen (red; n = 71) versus patients receiving no antipyretic treatment (n = 315). Graphs display mean differences in temperature decreases between each two comparators (no medication versus dipyrone, no medication versus acetaminophen and dipyrone versus acetaminophen) with their respective 95% confidence intervals. …”

Decreasing fluconazole susceptibility of clinical South African <i>Cryptococcus neoformans</i> isolates over a decade by Serisha D. Naicker (8648370)

Estimated mean values for light interception. by Torsten Schober (20485754)

“…However, with increasing PD, the proportion of higher-concentrated inflorescence fractions from upper canopy layers increased from 46% to 68%, while an extension of DVP shifted this proportion only marginally from 45% to 50%. In the context of standardized production, we therefore advocate high-density production systems that increase the proportion of desired inflorescence fractions from upper canopy layers.…”

S1 Dataset - by Nelmo Jordão Manjate (16324619)

Subjects:

Numbers of all fractures, arranged in order of decreasing mean age and with proportion of each of the fracture types in patients over 50, 65, and 75 years of age. by Camilla Bergh (9904522)

“…<p>Numbers of all fractures, arranged in order of decreasing mean age and with proportion of each of the fracture types in patients over 50, 65, and 75 years of age.…”

Detailed meta–analyses in untreated hypothyroid patients compared with controls for each HRV parameters: RR intervals, SDNN, RMSSD, pNN50, TP, LF, HF, LF/HF. by Valentin Brusseau (12769704)

Subjects:

Homogenizing experiment: Mean fecal egg counts (FECs), mean relative differences from the grand mean FECs per sample (over 7 observations), standard deviation of the relative diffe... by Felix Bosch (10024550)

Evaluation index results for the JS-10 phantom. by Masakazu Tsujimoto (22339504)

Subjects:

NEMA IEC body phantom. by Masakazu Tsujimoto (22339504)

Subjects:

Relationship between contrast, noise, and CNR. by Masakazu Tsujimoto (22339504)

Subjects:

JS-10 phantom. by Masakazu Tsujimoto (22339504)

Subjects:

The raw data used for the analyses in this study. by Masakazu Tsujimoto (22339504)

Subjects:

K-means++ clustering algorithm. by Zhen Zhao (159931)

Subjects:

Numbers of all fractures occurring in men, arranged in order of decreasing mean age, and with proportion of each of the fracture types in patients over 50, 65, and 75 years of age. by Camilla Bergh (9904522)

“…<p>Numbers of all fractures occurring in men, arranged in order of decreasing mean age, and with proportion of each of the fracture types in patients over 50, 65, and 75 years of age.…”

Numbers of all fractures occurring in women, arranged in order of decreasing mean age, and with proportion of each of the fracture types in patients over 50, 65, and 75 years of age. by Camilla Bergh (9904522)

“…<p>Numbers of all fractures occurring in women, arranged in order of decreasing mean age, and with proportion of each of the fracture types in patients over 50, 65, and 75 years of age.…”

First and second mean values and intraclass correlation coefficients (ICCs) of each parameter in pattern electroretinogram. by Soo Ji Jeon (3364214)

Identification of Gut Microbiota Biomarker Genera Distinguishing Wild and Captive Animals (A) Top 50 discriminative genera selected by random forest classifier, ranked by mean decrease accuracy. by Lin Xu (94491)

Means for STEM competence for pre- and post-test. by Esther Kim (2996622)