Scinapse 2017-2018 Undergraduate Science Case Competition: What the Frack is this?
Bibliographic record
Abstract
The Scinapse Undergraduate Science Case Competition (USCC) allows undergraduate students to experience the development of a new research proposal based off a given topic. A case is presented to all participants and using an in-depth literature search (publications, reports, studies and published writings), students connect and pinpoint key elements allowing them to construct a hypothesis in support of the case in question. The participants will then design a controlled experiment to determine whether their hypothesis is valid or not. Students register in teams of 2-4. The first round of the USCC involves the submission of a written proposal. The top 10% of proposals will be invited to uOttawa to compete in a poster competition against finalists of other universities from across the province. This year’s topic was hydraulic fracturing, a process that entails many environmental and health related challenges. The 2018 USCC attracted 743 students from 9 universities across the country, the top 25% of written submissions are highlighted in this abstract booklet. Scinapse is part of a larger organization, Undergraduate Research Initiative (URI), with the primary goal of inciting interest in research and developing related skills. More information on Scinapse and URI can be found at http://scinapsescience.com and http://uri-irpc.ca.
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 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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".