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Record W1656341315 · doi:10.1029/2008gb003416

Groundfish overfishing, diatom decline, and the marine silica cycle: Lessons from Saanich Inlet, Canada, and the Baltic Sea cod crash

2009· article· en· W1656341315 on OpenAlexaffabout
Timor Katz, Gitai Yahel, Ruthy Yahel, Verena Tunnicliffe, Barak Herut, Paul V. R. Snelgrove, John Crusius, Boáz Lazar

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsMemorial University of NewfoundlandOcean Networks Canada SocietyUniversity of Victoria
Fundersnot available
KeywordsGroundfishOceanographyOverfishingPopulationDiatomEnvironmental scienceGadusWater columnFisheryGeologyBiologyFisheries managementFishing

Abstract

fetched live from OpenAlex

In this study, we link groundfish activity to the marine silica cycle and suggest that the drastic mid‐1980s crash of the Baltic Sea cod ( Gadus morhua ) population triggered a cascade of events leading to decrease in dissolved silica (DSi) and diatom abundance in the water. We suggest that this seemingly unrelated sequence of events was caused by a marked decline in sediment resuspension associated with reduced groundfish activity resulting from the cod crash. In a study in Saanich Inlet, British Columbia, Canada, we discovered that, by resuspending bottom sediments, groundfish triple DSi fluxes from the sediments and reduce silica accumulation therein. Using these findings and the available oceanographic and environmental data from the Baltic Sea, we estimate that overfishing and recruitment failure of Baltic cod reduced by 20% the DSi supply from bottom sediments to the surface water leading to a decline in the diatom population in the Baltic Sea. The major importance of the marginal ocean in the marine silica cycle and the associated high population density of groundfish suggest that groundfish play a major role in the silica cycle. We postulate that dwindling groundfish populations caused by anthropogenic perturbations, e.g., overfishing and bottom water anoxia, may cause shifts in marine phytoplankton communities.

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 score0.159
Threshold uncertainty score0.630

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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

Citations23
Published2009
Admission routes2
Has abstractyes

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