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Record W1966577634 · doi:10.1525/abt.2010.72.9.4

Viewing Plant Systematics through a Lens of Plant Compensatory Growth

2010· article· en· W1966577634 on OpenAlexafffund
Roy V. Rea, Hugues B. Massicotte

Bibliographic record

VenueThe American Biology Teacher · 2010
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of Northern British Columbia
FundersUniversity of British ColumbiaUniversity of Northern British Columbia
KeywordsPlant growthIdentification (biology)Compensation (psychology)PhenomenonPlant taxonomyBiologyPlant biologyPlant identificationShootBotanyPlant speciesEcologyPsychologySystematicsEpistemologySocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Plant compensatory growth is a phenomenon of exaggerated vegetative growth that occurs in plants as a result of mechanical damage (e.g., cutting or browsing). Because shoots, leaves, and other plant parts grow larger on plants undergoing compensation, they typically fall outside of the normal ranges given in plant identification keys and confuse students who are attempting to classify them. Here, we describe the conundrum faced by students collecting compensatory materials and offer suggestions on how to help students identify their “plant-in-hand” and how to seize a teaching moment to examine and explain the underlying processes that lead to this fascinating plant response.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0030.023
Scholarly communication0.0060.010
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.062
GPT teacher head0.328
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2010
Admission routes2
Has abstractyes

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