Introducing Fundamental Microscopy Skills in a “Stage Wise” Manner to First Year Biology Students
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.032 |
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".