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Record W2804843398 · doi:10.1139/cjb-2018-0004

Regeneration responses differ among three boreal mosses after exposure to extreme temperatures

2018· article· en· W2804843398 on OpenAlexafffundvenue
Carlos J. Pasiche‐Lisboa, René J. Belland, Michele D. Piercey‐Normore

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBryophyte Studies and Records
Canadian institutionsMemorial University of NewfoundlandUniversity of AlbertaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological Sciences
KeywordsBiologyMossBiological dispersalPropaguleRegeneration (biology)EcologyBryophyteBotanyTaigaPopulation

Abstract

fetched live from OpenAlex

Many factors may affect the survival and establishment of a moss’s vegetative propagules after dispersal, but little is known about the species-specific nature of the response. This study examined the survival and regeneration of gametophore fragments after exposure to temperature changes for three boreal forest mosses from different habitats: Dicranum polysetum, Orthotrichum obtusifolium, and Pleurozium schreberi. Fragments were cultured on water agar and the survival and regeneration responses were recorded. Logistic regression analyses and AIC modeling evaluated the association between the response with the size of the gametophore fragments exposed to five abrupt or gradual temperatures for up to six exposure durations. The increased survival and regeneration was best explained when species were exposed to gradual, rather than abrupt temperatures; lower, rather than higher temperatures; and when the fragments had larger, rather than smaller sizes. The mosses had different survival and regeneration responses that may be species-specific, including clonal growth via the production of gametophore branches and protonemata, or mostly protonemata, even when exposed to elevated temperatures.

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 score0.496
Threshold uncertainty score0.896

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 head0.020
GPT teacher head0.218
Teacher spread0.198 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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