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Record W4231745830 · doi:10.5383/ijtee.07.02.009

Polar Auxin Transport: Understanding the Effects of Temperature and Precipitation on Polar Auxin Transport in Polytrichum Ohioense Could Develop a Moss into a Climate Change Bioindicator

2014· article· en· W4231745830 on OpenAlexvenueno aff
Dorothy Belle Poli, Bryan Piatkowski, Geoffrey Bader, Jessica Branning, Laura Kellam, Matthew Corbicz, Anne Wallace

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBryophyte Studies and Records
Canadian institutionsnot available
FundersThomas F. and Kate Miller Jeffress Memorial Trust
KeywordsAuxinBryophyteMossSporophytePrecipitationPolar auxin transportBotanyPolarEnvironmental scienceBiologyArabidopsisGeographyMeteorologyBiochemistryPhysics

Abstract

fetched live from OpenAlex

In an attempt to better understand polar auxin transport in the sporophytes of Polytrichum ohioense, a 5.3 fold increase in basipetal and acropetal transport was observed when temperature and precipitation varied significantly throughout the growing seasons. Wild plants were transferred into cultures and allowed to develop in temperatures that represented a “Warm Summer, Cold Winter” condition. Spores were grown on soil and watered to represent precipitation of a “Wet Fall, Winter” year. Traditional polar auxin transport assays were done on all sporophytes to calculate the amount of auxin transported in a polar fashion. The amount of water available to the developing sporophyte appears to be critical to polar auxin transport physiology; the temperature has less of an effect. This study also suggests that as climate change occurs, moss will be unequally affected by the environmental factors. An additional benefit of this study suggests that polar auxin transport assays may be developed to monitor climatic change. Keywords: Polar auxin transport, Bryophytes, Polytrichum ohioense, climate change, monitoring

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 score0.764
Threshold uncertainty score0.181

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 head0.006
GPT teacher head0.181
Teacher spread0.174 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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