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USE OF PULSE‐AMPLITUDE‐MODULATED FLUORESCENCE TO ASSESS THE PHYSIOLOGICAL STATUS OF<i>CLADOPHORA</i>SP. ALONG A WATER QUALITY GRADIENT<sup>1</sup>

2008· article· en· W2157296091 on OpenAlexaffabout
Véronique P. Hiriart‐Baer, Tim J. Arciszewski, Sairah Y. Malkin, Stephanie J. Guildford, Robert E. Hecky

Bibliographic record

VenueJournal of Phycology · 2008
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsUniversity of New BrunswickUniversity of WaterlooEnvironment and Climate Change Canada
Fundersnot available
KeywordsBiologyCladophoraPulse (music)FluorescenceAmplitudeQuality (philosophy)BotanyOpticsAlgaePhysics

Abstract

fetched live from OpenAlex

This study investigated the application of pulse-amplitude-modulated (PAM) fluorometry as a rapid assessment of benthic macroalgal physiological status. Maximum quantum efficiency (Fv /Fm ), dark-light induction curves, and rapid fluorescence light-response curves (RLC) were measured on the filamentous macroalgal Cladophora sp. from Lake Ontario on 5 d at 16 sites spanning a gradient of light and nutrient supply. For Cladophora sp. growing in situ, light limitation was assessed by comparing average daily irradiance with the light utilization efficiency parameter (α) derived from RLCs. In this study, there was a nonlinear relationship between Fv /Fm and the degree of P limitation in macroalgae. However, only light-saturated Cladophora sp. showed a significant positive linear relationship between Fv /Fm and P nutrient status. The absence of this relationship among light-limited algae indicates that their photosynthetic rate would be stimulated by increased water clarity, and not by increased P supply. PAM fluorescence measures were successfully able to identify light-saturated macroalgae and, among these, assess the degree to which they were nutrient limited. These results enable us to test hypotheses arising from numeric models predicting the impact of changes in light penetration and nutrient supply on benthic primary production.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.412

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 head0.058
GPT teacher head0.264
Teacher spread0.206 · 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

Citations25
Published2008
Admission routes2
Has abstractyes

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