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Record W1975938102 · doi:10.1021/ed4005968

The Advanced Interdisciplinary Research Laboratory: A Student Team Approach to the Fourth-Year Research Thesis Project Experience

2014· article· en· W1975938102 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Chemical Education · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto MississaugaConnaught Fund
KeywordsUndergraduate researchIdentification (biology)Work (physics)Project managementAgile software developmentHigher educationEngineering ethicsEngineeringEngineering managementPsychologyMathematics educationMedical educationSystems engineeringMedicinePolitical scienceEcologyMechanical engineering

Abstract

fetched live from OpenAlex

The advanced interdisciplinary research laboratory (AIRLab) represents a novel, effective, and motivational course designed from the interdisciplinary research interests of chemistry, physics, biology, and education development faculty members as an alternative to the independent thesis project experience. Student teams are assembled to work toward the completion of an interdisciplinary research project. Each team is composed of at least one student from a differing area of specialization (e.g., biology, biotechnology, chemistry, and physics), and projects are based on current trends in research. The inaugural project was the development of a portable DNA sequencer for in-field species identification; a project at the interface between physical and life sciences and that holds the potential for real-world application and positive social change. This work describes the details underlying the design and implementation of the AIRLab course, and includes an account of the method of student team assembly, the selection of a suitable research challenge, the specialized training provided in Agile project management, the methods used to achieve cohesive team dynamics, the learning outcomes from this experience, future directions that will be pursued for course improvement, and how our undergraduate degree-level expectations were met at an advanced level.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.434
Teacher spread0.393 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it