Mountain Scholar

Mountain Scholar is an open access repository service that collects, preserves, and provides access to digitized library collections and other scholarly and creative works from Colorado State University and the University Press of Colorado. It also serves as a dark archive for the Open Textbook Library.

Communities in Mountain Scholar

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Colorado State University, Fort Collins 26575
Explore the Colorado State University community’s scholarly output as well as items from the University at large and the CSU Libraries.
Open Textbook Library Archive 25
A limited number of titles are available here. To see all OTL titles, please visit the Open Textbook Library at https://open.umn.edu/opentextbooks. Only Open Textbook Library staff have access to all OTL Archive titles held in Mountain Scholar.
University Press of Colorado 1067
Access is limited to University Press of Colorado members. Non-members: to purchase books, please visit https://upcolorado.com/.

Recent Submissions

Access status: Open Access ,
National Hockey League prospect evaluation: utilizing machine learning to predict amateur prospect career success
(2025) Graff, Aaron, author; Nielsen, Aaron, advisor; Edmondson, Stacy, committee member
Evaluation of prospect potential at a professional level is crucial to building a championship roster in any sport. In the National Hockey League (NHL), prospect evaluation is just as critical, but even more difficult than in other sports, due to the plethora of amateur leagues globally and the youth of most prospects. This report summarizes the process of collecting North American amateur skater data between the 2007 and 2024 hockey seasons, alongside player draft position, and corresponding career expected goals in the NHL, all obtained to analyze predictive accuracy of several machine learning algorithms in predicting career expected goals. Player physical attributes, such as height and weight, were incorporated with amateur statistics from their final season before being drafted as predictor variables. Overall, bagged trees and random forest modeling had the smallest error in expected goal predictions on the test dataset. Such a model could be further developed and used as an evaluation tool for predicting prospects' NHL career success.
Access status: Open Access ,
Colorado Ongoing Basin Emissions (COBE) updated final report
(2025-11-20) Brown, Jenna A., author; Moy, Michael, author; Santos, Arthur, author; Rimelman, Ethan, author; Mollel, Winrose, author; Khaliukova, Olga, author; Okenberg, Callan, author; Daniels, William S., author; Hammerling, Dorit M., author; Zimmerle, Daniel, author; Hodshire, Anna L., author
The Colorado Ongoing Basin Emissions (COBE) project was jointly developed between teams at Colorado Department of Public Health and Environment (CDPHE)'s Air Pollution Control Division (APCD) and Colorado State University (CSU)'s Methane Emissions Technology Evaluation Center (METEC) to help inform the 2026 Colorado greenhouse gas (GHG) Intensity Verification Rule. The project is also intended to help inform the implementation of the GHG Intensity Verification Rule for calendar year 2026 and beyond. COBE had three primary objectives: • Collect representative measurements of methane emissions from upstream oil and gas facilities throughout the state of Colorado via anonymous aerial campaigns. • Develop measurement informed inventory (MII)s using the aerial emissions data. • Compare the MIIs to operator-reported emissions in the Oil and Natural Gas Annual Emission Inventory Reporting (ONGAEIR) to provide recommended ratios of modeled total emissions to corresponding reported emissions. To collect aerial measurements, the project worked with Bridger Photonics, Inc. (Bridger), GHGSat, and Insight M. METEC formed a scientific modeling team with Colorado School of Mines (CSM). METEC's modeling approach used a discrete event simulation tool via the Mechanistic Air Emissions Simulator (MAES). MAES is intended to first match a reported inventory, here ONGAEIR [1], and then add in any measurements of emissions that are determined to not be included in the reported emissions. If there is missing key information in ONGAEIR the facility cannot be modeled in MAES, which was the case for 19% of facilities for this study. While 81% of ONGAEIR upstream facilities that were operating, or partially operating, were modeled in MAES. MAES allows understanding of emissions at the emitter level (most often, equipment level). CSM concurrently developed a statistical model that relied only on the emissions detections by the measurement technologies, using various data sets to inform emissions below the detection limits of the aerial companies, including one of emission estimates derived from continuous monitoring systems at facilities included in the study and two from the recent literature. Both models developed emissions totals and estimated ratios of total modeled emissions to reported emissions. These ratios were further split out by major basins and major facility classification. The CSM statistical model predicted higher state-wide emissions totals and ratios than the MAES model. It estimated emissions between 87,210 and 134,352 mt/y and ratios of 3.30 to 5.09 (depending on the below-threshold dataset used) when using the same subset of ONGAEIR facilities as the MAES model, and emissions of between 109,364 and 167,848 mt/y with ratios of 3.81 to 5.85 when using all ONGAEIR facilities. In comparison, MAES estimated emissions of 38,936 mt/y and a ratio of 1.47. These results are based on the 2024 ONGAEIR dataset and provide an update to a previous version of this report based on the 2022 ONGAEIR dataset. In addition to updating MII results to the 2024 ONGAEIR, this updated report includes: • The contribution of various emission rates to the MAES model total, showing the importance of small emissions (<5 kg/h) • Additional methods for estimating emissions below aerial threshold in the CSM model More work will be done by the science team in COBE-2 to provide a comprehensive method reconciliation between the two models developed in COBE. COBE-2, funded via the Mark Martinez and Joey Irwin Memorial fund, will additionally develop recommended default factors for 2027. Similar to COBE, a public report will be disseminated near the end of 2026.
Access status: Open Access ,
Colorado's water year 2025 in review
(2025) Colorado Climate Center, author
Access status: Open Access ,
December 2025 Colorado monthly climate summary
(2025-12) Colorado Climate Center, author
Access status: Open Access ,
Fort Collins Weather Station monthly summary, August 2025
(2025-08) Colorado Climate Center, author