The Big and Small Of It: Using Large Scale Social Data and Small Scale Rna-Seq Data
Bibliographic record
Abstract
PURPOSE: To fulfill the requirements to receive an MS in Biostatistics at Grand Valley State University all students must complete a 440-hour internship. To help satisfy this requirement I completed two separate 400+ hour internships, one at the Ottawa County Department of Public Health, the other at Van Andel Research Institute. CHALLENGE: At each internship, I had to use large scale social data or small scale RNA-seq data. These required separate statistical techniques and research was conducted on how to best apply differing processes. EXPERIENCE: The goals of these internships were to further my skills as a biostatistician. I was expected to analyze data sets, clean data, produce results, write reports, and convey my findings, all while using a statistical programming package such as SAS or R. OUTCOME: Through both internships, I was able to meet all of the expected objectives while increasing my knowledge of statistics, data mining, and statistical programming. IMPACT: The data I analyzed is now being used by local entities to develop programs for youth living in Ottawa County and the data mining techniques helped to form new hypotheses’ at VARI.
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.
How this classification was reachedexpand
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 | 0.003 | 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 | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".