Science Atlantic 2019 Aquaculture & Fisheries and Biology Conference
Bibliographic record
Abstract
Science Atlantic Aquaculture & Fisheries and Biology (AF&B) conference is held annually by the Science Atlantic Biology committee in the conjunction of Aquaculture and Fisheries Committee, which allows undergraduate students to present their results of research. In various research projects, students have chance to make hypotheses on this project, design experi-ments to accept or deny their hypotheses using real research methods, analyze the data and then draw their conclusions. This particular learning style is vital in post-secondary biology learning, which has been well impregnated in biology programs at all universities in the Maritime. AF&B conference not only promote this learning style, but also help undergraduates in science communication. Every year, awards are granted to the students giving the best research presentations at annual AF&B conference. 2019 AF&B conference was held at Crandall University on March 8th-10th, which attracted science stu-dents from 13 universities in all Maritime Provinces of Canada to participate. The following are conference abstracts, which are categorized into three parts: oral presentation in biology, oral presentation in Aquaculture & Fisheries and poster presentation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.218 | 0.062 |
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".