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Record W2904529276 · doi:10.3934/mbe.2019006

Dynamics of a periodic stoichiometric model with application in predicting and controlling algal bloom in Bohai Sea off China

2018· article· en· W2904529276 on OpenAlex
Da Song, Meng Fan, Ming Chen, Hao Wang

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.

Bibliographic record

VenueMathematical Biosciences & Engineering · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBloomAlgal bloomAlgaeNutrientLight intensityEnvironmental scienceChinaDynamics (music)EcologyOceanographyPhytoplanktonBiologyGeologyPhysicsGeography

Abstract

fetched live from OpenAlex

We develop a nonautonomous stoichiometric algal growth model incorporating a season-driven light intensity. We characterize the model dynamics by showing positive invariance, dissipativity, boundary dynamics, and internal dynamics. We use numerical simulations to uncover the impacts of the seasonal light intensity and the nutrient availability on the algal dynamics. We discuss two control methods, removing algae (RA) periodically and blocking nutrient (BN) input from rivers constantly, via ourmodeling approach. By comparison, the BNmethod is amore effective way to terminate algal bloom in Yellow Sea off China. The model dynamics can fit the Bohai Sea data well. Our model and analysis provide a possible explanation of seasonal algal bloom and give some measurements for controlling algal bloom in China's coastal regions.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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