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Record W4388033623 · doi:10.1038/s41467-023-42654-z

Fading regulation of diurnal temperature ranges on drought-induced growth loss for drought-tolerant tree species

2023· article· en· W4388033623 on OpenAlexaff
Xianliang Zhang, Tim Rademacher, Hongyan Liu, Lu Wang, Rubén D. Manzanedo

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsCTT Group (Canada)Université du Québec en OutaouaisCegep de Saint Hyacinthe
FundersHebei Agricultural UniversityNational Natural Science Foundation of China
KeywordsEnvironmental scienceDaytimeDrought toleranceClimate changeDiurnal temperature variationRange (aeronautics)Global warmingAtmospheric sciencesBiologyClimatologyEcologyAgronomy

Abstract

fetched live from OpenAlex

Warming-induced droughts caused tree growth loss across the globe, leading to substantial carbon loss to the atmosphere. Drought-induced growth loss, however, can be regulated by changes in diurnal temperature ranges. Here, we investigated long term radial growth responses of 23 widespread distributed tree species from 2327 sites over the world and found that species' drought tolerances were significantly and positively correlated with diurnal temperature range-growth loss relationships for the period 1901-1940. Since 1940, this relationship has continued to fade, likely due to asymmetric day and night warming trends and the species' ability to deal with them. The alleviation of reduced diurnal temperature ranges on drought-induced growth loss was mainly found for drought resistant tree species. Overall, our results highlight the need to carefully consider diurnal temperature ranges and species-specific responses to daytime and nighttime warming to explore tree growth responses to current and future warmer and drier climates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.021
GPT teacher head0.257
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2023
Admission routes1
Has abstractyes

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