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Record W2951393574 · doi:10.1111/1365-2745.13228

Climatic change only stimulated growth for trees under weak competition in central boreal forests

2019· article· en· W2951393574 on OpenAlex

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Ecology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsLakehead UniversityNatural Resources CanadaUniversity of British ColumbiaCanadian Forest Service
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCompetition (biology)Climate changeTaigaBorealEcologyGlobal changeBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Global change ecologists have often used trees under weak competition (e.g. dominant/codominant trees) to examine relationships between climatic change and tree growth. Scaling up these results to a forest relies on the assumption that the climatic change–tree growth relationship is not affected by tree‐level competition. Using permanent sample plot data from the central Canadian boreal region where warming did not result in water deficit, we tested the above‐mentioned assumption by looking at whether the relationship between climatic change and tree growth varied with tree‐level competition, which was quantified using a modified Hegyi competition index. We found that tree growth increased over time for trees under weak competition, but decreased for those under strong competition. The divergent temporal trends among trees under different levels of competition led to a non‐significant change in growth for our study plots. Growth increased with regional warming, atmospheric [CO 2 ] and water availability for trees under weak competition, but not for those under strong competition. Synthesis . Our results suggest that upscaling the growth responses of dominant/codominant trees to climate change to a forest or a region can lead to biased estimates. Tree‐level competition should be taken into account when expressing climatic change and tree growth relationships.

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)0.000
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 score0.059
Threshold uncertainty score0.958

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)0.0010.000

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 head0.028
GPT teacher head0.261
Teacher spread0.233 · 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