Improving dichotomous keys for undergraduate teaching
Bibliographic record
Abstract
University of Guelph undergraduate students have been struggling to independently identify macroinvertebrates using dichotomous keys in the Biology of Polluted Waters course (BIOL*4350). The course currently uses dichotomous keys that lack definitions of complex anatomical terms and illustrations that place features in the context of the whole organism. This results in taxonomic bias, whereby some macroinvertebrate families are ignored in subsampling, especially for Ephemeroptera (mayflies). This is of particular concern to biotic assessment of stream quality that uses Ephemeroptera as biological indicators. An updated dichotomous key for Ephemeroptera with illustrations and definitions of anatomical terms integrated within the text of the key was developed at the University of Guelph in Winter 2012. The generation of the key utilized a local macroinvertebrate collection, published literature and existing keys. The effectiveness of the updated key was tested against the BIOL*4350 key by comparing the number of correct identifications produced by undergraduate student volunteers using both keys. Additionally, the number of correct identifications by student volunteers who had previously taken BIOL*4350 (n=18) and those who had not taken the course (n=40) were compared. It was predicted that students who had previously taken BIOL*4350 would produce more correct identifications than students who had not. The new key had a significantly higher proportion of correct identifications than the old key (p
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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.022 |
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".