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Record W2987363920 · doi:10.1126/science.aay5945

Catchment properties and the photosynthetic trait composition of freshwater plant communities

2019· article· en· W2987363920 on OpenAlexaff
Lars Lønsmann Iversen, Anders Winkel, Lars Baastrup‐Spohr, Anne Bækbo Hinke, Janne Alahuhta, Annette Baattrup‐Pedersen, Sebastian Birk, Peter Brodersen, Patricia A. Chambers, Frauke Ecke, Tõnnu Feldmann, Daniel Gebler, Jani Heino, Thomas Sand Jespersen, S. Jannicke Moe, Tenna Riis, Laura Sass, Ole Vestergaard, Stephen C. Maberly, Kaj Sand‐Jensen, Ole Pedersen

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersNatural Environment Research CouncilNarodowa Agencja Wymiany AkademickiejCarlsbergfondet
KeywordsTraitPhotosynthesisComposition (language)Environmental scienceDrainage basinEcologyCatchment areaBiologyGeographyBotanyComputer scienceCartography

Abstract

fetched live from OpenAlex

Change in plants as bicarbonate rises Freshwater plants can be broadly divided into two major categories according to their photosynthetic traits: Some use carbon dioxide as their carbon source, whereas others use bicarbonate. Iversen et al. found that the relative concentrations of these two inorganic carbon forms in water determine the functional composition of plant communities across freshwater ecosystems (see the Perspective by Marcé and Obrador). They created global maps revealing that community composition is structured by catchment geology and not climate (in contrast to the terrestrial realm, where the trait composition is structured by temperature and rainfall). Anthropogenic influences from land-use change are causing large-scale increases in bicarbonate concentrations in freshwater catchments and are thus leading to wholesale changes in the composition of their aquatic plant communities. Science , this issue p. 878 ; see also p. 805

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.012
GPT teacher head0.185
Teacher spread0.174 · 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

Citations133
Published2019
Admission routes1
Has abstractyes

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