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Record W3026739321 · doi:10.37590/able.v41.art56

Introducing Fundamental Microscopy Skills in a “Stage Wise” Manner to First Year Biology Students

2020· article· en· W3026739321 on OpenAlexaff
Charlotte de Araujo, Karen Joan Puddephatt, Gavin Ridgeway, Lynda H. McCarthy, Andrew E. Laursen

Bibliographic record

VenueAdvances in Biology Laboratory Education · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBiologyMicroscopyMathematics educationStage (stratigraphy)PsychologyPhysicsOpticsPaleontology

Abstract

fetched live from OpenAlex

The microscope is an ubiquitous tool in the undergraduate biology laboratory.Implementing a hierarchical approach, students were introduced to skills in microscopy.Initially, students attempted to locate and examine a specimen on a prepared slide.Subsequently, students employed the microscope to recognize and describe the stages of mitosis in plant cells, observing particular features for each phase in onion (Allium) root tip.During the session, students created a wet mount with Brown planaria (Dugesia tigrina) and practiced using the microscope to observe live organisms.While developing skills in microscopy, parallel laboratory activities also included creating scientific illustrations and quantifying the proportion of cells in each stage of the cell cycle, integrating research skills (gathering, analyzing, interpreting data) as part of the exercise.In addition, we have adapted peer-to-peer teaching, where upper year students created a video microscopy tutorial.This supplemental resource provided first year students with an overview of the standard procedures and key components of a microscope.After reviewing the video, a majority of biology students felt more confident and comfortable using a microscope and more aware of practices which contribute to improper use.Coupling in-laboratory and online resources promoted student development of practical techniques.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.356
Teacher spread0.349 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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