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The characterization of complex continuous norms of reaction

2007· article· en· W2087766875 on OpenAlexafffund
Andrew M. Simons, Ioan Wagner

Bibliographic record

VenueOikos · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlasticityPhenotypic plasticityBiologyLoessPopulationEcologyEvolutionary biologySelection (genetic algorithm)Computer sciencePaleontologyMachine learning

Abstract

fetched live from OpenAlex

Recent focus on the array of phenotypes expressed under differing environmental conditions, or phenotypic plasticity, has led to increased understanding of its genetic basis as well as its adaptive significance. However, the quantification of plasticity has proven difficult, hampered by both the limited number of environments over which plasticity may typically be assessed and by the need to assume, a priori, the general form of reaction norms under study. Our understanding of the shapes of continuous norms of reaction and, consequently, the subtle differences that may exist in shapes among genotypes or populations is rudimentary. Here, we propose the use of the loess smoothing function to analyze complex norms of reaction and to quantify total plasticity over many environments. A thermogradient incubator offers an ideal means to provide many environments for a demonstration of the use of the loess method. We test seed germination in three populations of two monocarpic plant species for population differentiation in plasticity to temperature. First, we test for differentiation in norms of reaction to 30 temperature environments among three populations of the monocarpic perennial, Lobelia inflata . The second demonstration assesses plasticity to eight temperature environments of three populations of the arctic‐alpine annual, Koenigia islandica . Our demonstration shows that the loess technique can detect significant genetic differentiation among populations in complex norms of reaction for both species studied, and suggests that the use of this procedure should be considered where the form of norms of reaction might be complex. The general applicability of the approach is discussed.

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 score0.683
Threshold uncertainty score0.078

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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

Citations11
Published2007
Admission routes2
Has abstractyes

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