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Deep chlorophyll maxima and UVR acclimation by epilimnetic phytoplankton

2010· article· en· W1545028831 on OpenAlexafffundabout
Joel W. Harrison, Ralph E. Smith

Bibliographic record

VenueFreshwater Biology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhytoplanktonAcclimatizationPhotosynthesisPhotosynthetically active radiationChlorophyll aChlorophyllFluorometerIrradianceEnvironmental scienceBiologyChlorophyll fluorescenceEcologyEnvironmental chemistryBotanyNutrientAtmospheric sciencesChemistryPhysicsFluorescence

Abstract

fetched live from OpenAlex

1. It is well established that ultraviolet radiation (UVR) has many harmful effects on phytoplankton, but the factors controlling algal sensitivity to UVR are not fully understood. 2. We exposed phytoplankton communities from the epilimnia and deep chlorophyll maxima (DCM) of 2 Canadian lakes to 14 irradiance treatments of various spectral quality and monitored changes in the maximum quantum efficiency of Photosystem II photochemistry (Fv/Fm) using a pulse amplitude modulation fluorometer. 3. Phytoplankton from DCM did not show marked differences from epilimnetic communities in taxonomy or nutrient status, but exhibited substantially higher photosynthetic impairment under UVR exposure. 4. Our results suggest that epilimnetic phytoplankton acclimate to in situ light conditions in a spectrally-specific manner, and that ultraviolet-A radiation is a stronger stressor than ultraviolet-B or photosynthetically active radiation in the mixed layers of our study lakes. Model estimates of damage and recovery rate constants revealed that the phytoplankton of the two lakes relied upon different strategies of UVR-acclimation, in one lake minimising susceptibility to photodamage and in the other maximising recovery efficiency.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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 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

Citations17
Published2010
Admission routes3
Has abstractyes

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