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Record W2938601188 · doi:10.26685/urncst.142

Science Atlantic 2019 Aquaculture & Fisheries and Biology Conference

2019· article· en· W2938601188 on OpenAlexaffabout
Zhan Yang, Russell H. Easy

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsAcadia UniversityCrandall University
Fundersnot available
KeywordsPresentation (obstetrics)AquacultureFisheries scienceFisheries ResearchStyle (visual arts)FisheryLibrary scienceFish <Actinopterygii>Fisheries managementBiologyComputer scienceGeographyMedicineFishing

Abstract

fetched live from OpenAlex

Science Atlantic Aquaculture &amp; Fisheries and Biology (AF&amp;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&amp;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&amp;B conference. 2019 AF&amp;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 &amp; 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 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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.012
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.069
GPT teacher head0.382
Teacher spread0.313 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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