Annotate based on these instructions, tell me what to annotate and provide a comment for each annotation: Provide enough comments to indicate you read the entire article and were thoughtful about it. Feeling unsure what to say? Look over the slides from Week 1 "How to read a scientific article," and consider addressing these topics: 1) The major research question(s) explored. 2) What prior research was done? What research gaps does the author intend to fulfill? 3) What are their hypotheses? 4) How does the author address these gaps (i.e., methods)? 5) What's the main finding of each figure? 6) If you can choose, what is the ONE piece of data that was most important or directly addressing the question? 7) Do the author's interpretations match the evidence? (It's OK to disagree!) 8) Any critiques or further comments on the paper.

numer We also created an Al judge using the same local LLM model, and asked it to evaluate essays in the same way human teachers did. We gave a system prompt that defined the Al judge as the writing expert. We found the Al judge was more statistically inclined to evaluate everything around a score of 4. See the distribution in Figure 40 below. Uniqueness . ' * . . Accuracy * . . . * } ae a es * ChatGPT . * : Content . Language and Style Structure Organization oo =e ee v oe! * oo tees + 4 oe - \" * se . . . . . 1 1.5 2 2.5 3 3.5 4 4.5 5 Al Figure 40. Al judge vs Human-Teacher Assessments Distribution. This scatter plot compares the average rankings given by human teachers and Al (LLM model) across different essay metrics. The X-axis represents the average scores assigned by the Al judge, while the Y-axis represents the average scores given by human teachers. Each dot on the plot corresponds to a specific essay metric, with the color of the dots differentiating between the metrics. On average, human teachers assigned smaller scores to each metric except the ChatGPT metric, where teachers could not say exactly the LLMs were used to write the essays, however the Al judge assessed almost half of the essays as those that were written with the help of LLMs. See Figure 41 below. Assessors: \"Ml - HIUMAN Groups: 'uM Search Engine Brain Only 'Sessions: $1 $2 S23 S4 Metrics: Accuracy ChatGPT + Language and Style choices courage forethought happiness perfect philanthropy Figure 41. Al judge vs Human Teacher Assessments. This figure compares LLM-based Al assessments with human teacher evaluations for the essays across various metrics. The Y-axis shows the average scores assigned by each assessor, with the comparison highlighting consistency and discrepancies between Al and human judgments on the same set of essays. Solid color bars show Al judge assessments, while dashed overlaid bars show human-teacher 63