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Modeling the bacterial contribution to planktonic community respiration in the regulation of solar energy and nutrient availability

2015· article· en· W987857670 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEcological Complexity · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsAlgaeNutrientOrganic matterPlanktonBiologyMicrobial loopEcosystemEnvironmental scienceEcologyPhytoplankton

Abstract

fetched live from OpenAlex

In planktonic ecosystems, algae and bacteria exhibit complex interrelationships, as algae provide an important organic matter source for microbial growth while microbial metabolism recycles limiting nutrients for algae in a loose commensalism. However, algae and bacteria can also compete for available nutrients if supplies of organic matter are sufficient to satisfy bacterial demand. We developed a stoichiometrically explicit model of bacteria–algae interactions that incorporated realistic assumptions about algal light and nutrient utilization, algal exudation of organic matter, and bacterial processing of organic matter and nutrients. The model makes specific predictions about how the relative balance of algae and bacteria should change in response to varied nutrient and light availability seen in lakes and oceans. The model successfully reproduces published empirical data and indicates that, under moderate nutrient supply, the bacterial percentage of total respiration should be maximal at intermediate light intensity.

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.

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)0.003
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 score0.152
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.086
GPT teacher head0.246
Teacher spread0.160 · 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