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Record W1978881201 · doi:10.1139/cjb-2014-0085

Using among-year variation to assess maternal effects in<i>Pinus aristata</i>and<i>Pinus flexilis</i>

2014· article· en· W1978881201 on OpenAlexaffvenue
Erin Borgman, Anna W. Schoettle, Amy L. Angert

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersU.S. Forest ServiceRocky Mountain Research StationColorado State University
KeywordsSeedlingBiologyTwigPinus <genus>Maternal effectOffspringBotanyHorticulture

Abstract

fetched live from OpenAlex

Maternal effects, the effect of the maternal environment during development on offspring growth, can complicate the interpretation of common garden studies. Growing one or more generations in a common environment can help minimize maternal effects, but is often not practical with long-lived species. In Pinus aristata Engelm. and Pinus flexilis James, we assessed maternal effects by growing offspring sourced over multiple years from the same mother trees, comparing growth traits between source years. Additionally, we explored the effect of maternal environment on seed characteristics by collecting five twig clippings from each mother tree and measuring characteristics indicative of the relative vigor of the tree during each seed source year. The effect of year was significant for twig growth characteristics, seed size, and seedling performance. For both species, there were significant relationships between the relative inter-annual (RIA) variation in seed mass and the RIA variation in numerous seedling traits including cotyledon length, seedling total dry mass, and needle length. Variation in seed mass was not predicted by yearly variation in the maternal plant’s phenotypic traits. These results support the hypothesis that maternal effects translate into variation in early seedling growth and suggest possibilities to statistically account for them in common garden studies involving long-lived species.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.439

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 head0.013
GPT teacher head0.249
Teacher spread0.237 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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