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Record W2158755270 · doi:10.21083/surg.v7i1.2750

Improving dichotomous keys for undergraduate teaching

2014· article· en· W2158755270 on OpenAlexaffvenueabout
Lisa Vollbrecht, Marie Thérèse Rush, Karl Cottenie

Bibliographic record

VenueSURG Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsKey (lock)Context (archaeology)EcologyComputer scienceMathematics educationBiologyPsychology

Abstract

fetched live from OpenAlex

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 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 designNot applicable
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

Citations1
Published2014
Admission routes3
Has abstractyes

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