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Record W4399698812 · doi:10.4257/oeco.2024.2802.01

PHOTOSYNTHETIC ADAPTATIONS AND NICHING OF PLANTS IN THE TROPICS: A QUARTER-CENTURY OF FIELDWORK AT A VARIETY OF PHYSIOGNOMIES OF THE CERRADOS AND ATLANTIC FOREST OF BRAZIL

2024· article· en· W4399698812 on OpenAlexaboutno aff
Ulrich Lüttge, Augusto C. Franco, Fábio Rúbio Scarano

Bibliographic record

VenueOecologia Australis · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsTropicsQuarter (Canadian coin)Variety (cybernetics)GeographyPhotosynthesisBiologyAgroforestryEcologyBotanyComputer scienceArchaeologyArtificial intelligence

Abstract

fetched live from OpenAlex

For about 25 years the photosynthetic performance of plants has been measured at a variety of different types of vegetation physiognomies of the cerrados and Atlantic forests of Brazil. The results of all these 28 publications, at the great majority of which Eduardo Arcoverde de Mattos has actively and most inspiringly participated, are overviewed here. A plea is made for an approach of physiological synecology with comparisons between different habitats, between species within given habitats and between given species across different habitats. This is exemplified for habitats within the geographical coordinates of 15°44’ and 23°08’S and 47°53’ and 41°15’ W. The sites, their environmental characteristics and typical vegetation physiognomy, are described. The observations and results of the ecophysiological field work with respect to performance of photosynthetic gas exchange and other traits, including morphotypic adaptations, viz. plant morphotypes, and water relations, are summarized. The, both facilitating and competing, performances of plants with C3-photosynthesis, and different levels of expression of crassulacean acid metabolism (CAM) are evaluated. Diversity and plasticity of niches and species is considered. The consequences for a “post-modern-synthesis” of evolution theory are assessed.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.206

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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
Published2024
Admission routes1
Has abstractyes

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