Developing a protocol for bioinformatics analysis: An integrated information behavior and task analysis approach: Research Articles
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
The purpose of this research is to capture, understand, and model the process used by bioinformatics analysts when facing a specific scientific problem. Integrating information behavior with task analysis, we interviewed 20 bioinformatics experts about the process they follow to conduct a typical bioinformatics analysis—a functional analysis of a gene, and then used a task analysis approach to model that process. We found that each expert followed a unique process in using bioinformatics resources, but had significant similarities with their peers. We synthesized these unique processes into a standard research protocol, from which we developed a procedural model that describes the process of conducting a functional analysis of a gene. The model protocol consists of a series of 16 individual steps, each of which specifies detail for the type of analysis, how and why it is conducted, the tools used, the data input and output, and the interpretation of the results. The linking of information behavior and task analysis research is a novel approach, as it provides a rich high-level view of information behavior while providing a detailed analysis at the task level. In this article we concentrate on the latter. © 2005 Wiley Periodicals, Inc.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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