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A revised classification system describing the ecological quality status of organically enriched marine sediments based on total dissolved sulfides

2020· article· en· W3014246414 on OpenAlexafffund
Peter J. Cranford, Lindsay M. Brager, Deanna Elvines, David Wong, Brent Law

Bibliographic record

VenueMarine Pollution Bulletin · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsBenthic zoneOrganic matterSedimentEnvironmental scienceEnvironmental chemistryWater qualityBenthosSulfideAquatic ecosystemEcologyChemistryBiology

Abstract

fetched live from OpenAlex

A field study is presented that provides an alternative method and system for classifying the ecological quality status (EQS) of organically enriched marine sediments based on total free sulfide concentrations (S2−). Sediments collected adjacent to coastal aquaculture activities across a broad biogeographic range were analysed using three S2− methods. S2− is a product of organic matter mineralization and is a major cause of benthic community impacts from excess organic enrichment. The results confirm that the ion-selective electrode protocol that is widely used in monitoring programs to classify benthic impacts provides unreliable data and site classifications. An EQS classification system is presented that employs S2− data measured rapidly and simply in the field by direct ultraviolet spectrophotometry. Interrelations between S2− concentrations and several benthic macrofauna community health metrics were employed to develop the EQS system. These relationships were consistent regardless of organic matter source, geographic region or sediment grain size.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.081
GPT teacher head0.263
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

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
Published2020
Admission routes2
Has abstractyes

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