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Record W6948261955 · doi:10.5061/dryad.0zpc866v8

Data from: Harsh environmental regimes increase the functional significance of intraspecific variation in plant communities

2020· dataset· en· W6948261955 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsIntraspecific competitionInterspecific competitionAbiotic componentPlant communitySpecies richnessSpecific leaf areaSpecies diversityTraitCompetition (biology)

Abstract

fetched live from OpenAlex

The relative importance of intraspecific trait variation (rITV) for functional diversity (FD) in plant communities is increasingly apparent, but the influence of abiotic factors on the balance between intraspecific and interspecific effects in contrasting environments is uncertain. We predicted that rITV would increase with environmental harshness as a result of decreasing interspecific variation (Inter_FD) and concomitant increases in between-population ITV(Intra_FD). We empirically tested this prediction in a comparison of rITV for five traits (mature plant height, SLA: specific leaf area, leaf dry matter content, and the foliar concentrations of nitrogen and phosphorus) characterizing species in Tibetan alpine meadow communities from contrasting environmental regimes(i.e. valley floor at the base of the ridge, northern- and southern-slopes at mid-elevation, and ridge top) on each of three sites (ridges). We found that across plots: i) rITV for all five traits increased with environmental harshness not only through decreasing Inter_FD but also increasing Intra_FD; ii) increases in rITV were mostly attributable to declines in soil resources, especially soil phosphorus, with lower soil P significantly associated with lower Inter_FD but higher Intra_FD for most traits; and iii) although neither Intra_FD nor Inter_FD were significantly impacted by species richness for any traits, a higher rITV for SLA was significantly associated with low species richness.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.032
Threshold uncertainty score0.997

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 science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.237
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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2020
Admission routes1
Has abstractyes

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