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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".