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Record W7014530371

Projected climate change impact on Baltic Sea cyanobacteria Climate change impact on cyanobacteria

2013· article· en· W7014530371 on OpenAlexaff

Bibliographic record

VenueMax Planck Digital Library · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsImpact
Fundersnot available
KeywordsCyanobacteriaPhytoplanktonClimate changeForcing (mathematics)Baltic seaGlobal changeBiomass (ecology)
DOInot available

Abstract

fetched live from OpenAlex

Compared to other phytoplankton groups, nitrogen-fixing cyanobacteria generally prefer high water temperatures for growth and are therefore expected to benefit from global warming. We use a coupled biological-physical model with an advanced cyanobacteria life cycle model to compare the abundance of cyanobacteria in the Baltic Sea during two different time periods (1969-1998; 2069-2098). For the latter, we find prolonged growth and a more than twofold increase in the climatologically (30 years) averaged cyanobacteria biomass and nitrogen fixation. Additional sensitivity experiments indicate that the biological-physical feedback mechanism through light absorption becomes more important with global warming. In general, we find a nonlinear response of cyanobacteria to changes in the atmospheric forcing fields as a result of life-cycle related feedback mechanisms. Overall, the sensitivity of the cyanobacteria-driven system suggests that biological-physical and life-cycle related feedback mechanisms are important and must therefore be included in future projection studies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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