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Record W4387226457 · doi:10.59720/19-107

Temperatures of 20°C Produce Increased Net Primary Production in Chlorella sp.

2020· article· en· W4387226457 on OpenAlexaff
Kiran J. Biddinger, Emma Bradley, Julie Seplaki

Bibliographic record

VenueJournal of Emerging Investigators · 2020
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsMilton District Hospital
Fundersnot available
KeywordsPhotosynthesisAutotrophChlorellaCarbon dioxidePrimary productionRuBisCOChemistrySugarBotanyAlgaeEnvironmental chemistryBiologyEcologyEcosystemBiochemistry

Abstract

fetched live from OpenAlex

Chlorella sp. are autotrophs that introduce stored energy into biological systems through photosynthesis. Net primary production (NPP) reflects the amount of energy converted to sugar bond energy in photosynthesis minus the amount of energy consumed by cellular respiration. Because carbon dioxide (CO2) acidifies water, the net CO2 production leads to a change in pH that reflects the NPP. Establishing a relationship between temperature and NPP could provide insights into maximizing the biological removal of CO2 from the atmosphere. In this experiment, the effect of temperature on the NPP of Chlorella sp. was measured as a function of ∆pH, which was determined using a standard curve of buffers of known pH versus absorbance. The ∆pH increased as the incubation temperatures increased towards 20°C, and then as the temperatures increased after 20°C, the ∆pH decreased, indicating that the NPP of the Chlorella sp. was maximized around 20°C. Around 20°C, the enzymes involved in photosynthesis, such as rubisco, may approach their optimal temperatures and thus be maximally efficient. As global warming is caused by excess CO2 in the atmosphere, Chlorella incubated at temperatures around 20°C would be maximally efficient at naturally removing CO2 through photosynthesis and could provide useful insights for new technologies to fight climate change.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.231
Teacher spread0.212 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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