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Record W2084997939 · doi:10.4319/lo.2008.53.6.2427

Seasonal changes in planktonic bacterivory rates under the ice‐covered coastal Arctic Ocean

2008· article· en· W2084997939 on OpenAlexaff
Dolors Vaqué, Òscar Guadayol, Francesc Peters, Jordi Felipe, Laia Angel-Ripoll, Ramón Terrado, Connie Lovejoy, Carlos Pedrós‐Alió

Bibliographic record

VenueLimnology and Oceanography · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBacterivoreSpring bloomPhytoplanktonBayMicrobial food webBiomass (ecology)ArcticSpring (device)Environmental scienceChlorophyll aPlanktonAnimal scienceBiologyNutrientEcologyOceanographyTrophic levelBotanyGeology

Abstract

fetched live from OpenAlex

Bacterivory was determined in surface waters of Franklin Bay, western Arctic, over a seasonal ice‐covered period (winter‐spring, 2003‐2004). The objectives were to obtain information on the functioning of the microbial food web under the ice, during winter (from 21 December 2003 to 21 March 2004) and during spring (from 22 March 2004 to 29 May 2004), and to test whether bacterial losses would increase after the increase in bacterial production following the spring phytoplankton bloom. Chl a concentrations ranged from 0.04 to 0.36 µg L −1 , increasing in March and reaching a peak in April. Bacterial biomass showed no consistent trend for the whole period, and protist biomass followed a pattern similar to that of Chl a . Bacterial production increased 1 week after Chl a concentrations started to increase, while bacterivory rates increased very slightly. Average bacterivory rates in winter (0.16 ± 0.07 µg C L −1 d −1 ) were not significantly different from those in spring (0.29 ± 0.24 µg C L −1 d −1 ). Average bacterial production, on the other hand, was similar to bacterivory rates in winter (0.19 ± 0.38 µg C L −1 d −1 ), but higher than bacterivory in spring (0.93 ± 0.28 µg C L −1 d −1 ). Therefore, bacterial production was controlled by grazers during winter and by substrate concentration in spring.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.978

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)0.0010.000

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 head0.190
Teacher spread0.177 · 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 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

Citations59
Published2008
Admission routes1
Has abstractyes

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