Board 373: Renewable Energy Systems Training (REST) Project Final Report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Renewable energy systems are more efficient and environmentally friendly power generation solutions as compared to traditional fossil generators, and as a result have created a continuously expanding job market. The global investment in solar PV systems has gone through a mostly increasing trend in the past ten years, which implies that the solar PV market requires a significant number of Science, technology, engineering, and mathematics (STEM) graduates specifically trained to handle the technical challenges and meet the job market demand. This project is funded through the Advanced Technological Education (ATE) program of National Science Foundation (NSF), and has been conducted at New Jersey Institute of Technology (NJIT) with the objective to train the required workforce for the solar photovoltaic (PV) job market through several activities that will provide benefits to university students, K-12 students, faculty members and instructors, and remote users all around the U.S. In this paper, the five major activities of the project are explained, which include: (i) Design and development of the new laboratory entitled "Renewable Energy Systems Training (REST)" and the associated new course entitled "Solar PV Planning and Installation", (ii) summer workshops for K-12 students through Center for Pre-College Programs (CPCP) at NJIT, (iii) faculty development workshops for the instructors of other 2- and 4-year institutions, (iv) undergraduate research and senior design projects, and (v) development of a dedicated public website to include all the lecture notes, laboratory experiments, video recordings, publications, guidelines to develop similar courses, and other instructional materials. This paper summarizes and presents the comments and feedback from external advisory committee (EAC), external evaluator, faculty development workshop participants, K-12 workshop participants, and the students enrolled in the new course. It also explains about the career placement, student retention, and community college transfer rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it