Additional file 1 of Assessment of care provision integration in a community-based mental health system: balanced care model implementation in Andalusia (Spain) by Diego Diaz-Milanes (17273399)

a-c. Key components of PrEP Service Delivery Models Implemented within DREAMS Programming in Khomas and Zambezi Regions of Namibia 2018–2019. by Gena Barnabee (12324431)

“…Key components of PrEP Service Delivery Models Implemented within DREAMS Programming in Khomas and Zambezi Regions of Namibia 2018–2019.…”

The class-wise performance metrics for the best CNN model, implementing word, position, part of speech and chunk embedding features, used for spice-disease relationship extraction. by N. K. Rakhi (5309756)

ZILLNB_Model by Qinhuan Luo (21288548)

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Model implementation for research article "Unraveling Soil Moisture Dynamics with Dual-Scale Interpretable Machine Learning: Cover Cropping and Irrigation Insights in Semi-Arid Agricultural Systems" by Huichao Yin (14589020)

“…<p dir="ltr">Model implementation for research article "Unraveling Soil Moisture Dynamics with Dual-Scale Interpretable Machine Learning: Cover Cropping and Irrigation Insights in Semi-Arid Agricultural Systems"</p>…”

<i>α</i>_<i>crit</i>, both predicted from the mean field theory as well as measured from the model implemented on regular, random networks. by Kaare B. Mikkelsen (2157820)

Maximum clade credibility, time-scaled phylogeny of CC180 serotype 3 (PMEN31) Clade II isolates estimated using the structured coalescent model implemented in BEAST2. by Taj Azarian (659326)

List of orthogroups associated with one of the genome assembly metrics (<i>variable</i> column) given the simultaneous, subsequent or terminal model implemented in the treeWAS R package (<i>mode</i> column). by Thomas Badet (4714992)

Estimates of the time of the most recent common ancestor (TMRCA) of <i>Rice yellow mottle virus</i> by Bayesian coalescent methods under several molecular clock and population genetic models implemented in BEAST by Denis Fargette (63317)

The mode and 95% confidence interval for <i>N_C</i>, <i>N_H</i> and <i>T</i> (in number of generations) for the demographic models implemented in DIYABC for the scenario with the highest probability (population bottleneck). by Nicolas Dussex (696163)

“…<p>The mode and 95% confidence interval for <i>N_C</i>, <i>N_H</i> and <i>T</i> (in number of generations) for the demographic models implemented in DIYABC for the scenario with the highest probability (population bottleneck).…”

Configuration details of the object detection models implemented in MMDetection. Here, FPN = Feature Pyramid Network, AHs = Attention Heads, TLs = Transformer Layers, SGD = Stochastic Gradient Descent.... by Parminder Kaur (719846)

“…<p>Configuration details of the object detection models implemented in MMDetection. Here, FPN = Feature Pyramid Network, AHs = Attention Heads, TLs = Transformer Layers, SGD = Stochastic Gradient Descent. …”

The Bayes Factor (BF) of each model, mode and 90% highest posterior density (in parentheses) for <i>N_C</i>, <i>N_H</i> and <i>T</i> for the demographic models implemented in MSVAR. by Nicolas Dussex (696163)

“…<p>The Bayes Factor (BF) of each model, mode and 90% highest posterior density (in parentheses) for <i>N_C</i>, <i>N_H</i> and <i>T</i> for the demographic models implemented in MSVAR.</p>…”

Critical transitions in networked spin-shift model implementation with 2000 nodes in groups of 400 with link degrees <i>deg</i> ∈ {3, 4, 5, 6, 7} respectively, and driven over 20000 iterations into an assortativity of 0.99. by Manfred Füllsack (4691716)

Challenges and solutions when implementing genetics and parasite data into models. by Doug Butterworth (10005953)

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Hyperspectral reflectance data, plants traits and weights for trained model implemented in: "Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel." https://doi.org/10.1002/ppj2.20106 by Michael C. Tross (12090261)

A process suggested for model implementation (1 –delegating the task to ISec experts; 2 –performing an assessment; 3 –data analysis according to the model design; 4 –presentation of the results and identification of gaps in the ISec performance; 5 –implementation of corrective measures). by Kaja Prislan (3131103)

“…<p>A process suggested for model implementation (1 –delegating the task to ISec experts; 2 –performing an assessment; 3 –data analysis according to the model design; 4 –presentation of the results and identification of gaps in the ISec performance; 5 –implementation of corrective measures).…”

Appropriate models. by James M. Osborne (163443)

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Table of parameters specific to the morphogen-dependent proliferation simulations. by James M. Osborne (163443)

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Simulations of monoclonal conversion in the colonic crypt. by James M. Osborne (163443)

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Table of parameters specific to the lateral inhibition simulations. by James M. Osborne (163443)

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