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Record W4310188930 · doi:10.1080/17550874.2022.2147804

Plant reproductive phenology along an elevation gradient in the extreme environment of the Canadian High Arctic

2022· article· en· W4310188930 on OpenAlex
Zoe A. Panchen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePlant Ecology & Diversity · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCanadian Museum of NatureUniversity of Ottawa
Fundersnot available
KeywordsPhenologyClimate changeArcticBiological dispersalGrowing degree-dayGlobal warmingEcologyElevation (ballistics)Environmental sciencePhysical geographyGeographyBiologyPopulation

Abstract

fetched live from OpenAlex

Background The extreme environment of the Canadian High-Arctic is experiencing unprecedented climate change with temperatures rising at three times the global average. There is a compelling need to understand how the phenology of Arctic plants will respond. However, long-term High-Arctic phenology monitoring is challenging due to the region’s remoteness.Aim To predict phenological responses of Arctic plants to climate change using an elevation gradient with associated temperature gradient as a proxy for climate change.Methods Flowering and seed dispersal times of seven Arctic species were recorded along an elevation gradient on Ellesmere Island, Nunavut, Canada in 2015 and related to air temperature measured at plant height and growing day degree (GDD).Results Flowering and seed dispersal times were earliest at the warmest site. A significant relationship with temperature was observed in flowering times of five species and seed dispersal times of one species. Conspecifics experienced fewer GDD at peak flowering at the coldest site than at warmer sites.Conclusions Temperature gradient observations provide insights into phenology–temperature relationships that complement long-term monitoring and enhance our ability to understand the impacts of climate change in remote regions. However, potential species adaptation along the temperature gradient should be taken into account. This single summer of results should be viewed with caution.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.049
GPT teacher head0.183
Teacher spread0.134 · 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