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Record W7078228942 · doi:10.5066/p13nf6td

Data from surface water and sediment nutrient processing experiments associated with the Maumee River (Toledo, OH) during the 2021 growing season

2025· dataset· en· W7078228942 on OpenAlexaff

Bibliographic record

VenueUSGS DOI Tool Production Environment · 2025
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsTrent University
Fundersnot available
KeywordsNutrientSedimentHydrology (agriculture)Growing seasonSurface waterUpstream (networking)Current (fluid)

Abstract

fetched live from OpenAlex

River-to-lake transitional zones or rivermouths are biogeochemically active sections of the aquatic continuum that are often understudied compared to their adjoining environments. Internal nutrient loading from these transitional zones may be a considerable source of nutrients to lakes and disconnect upstream management initiatives from in-lake improvements. To contextualize internal nutrient loading by rivermouth sediments, we conducted sediment core incubations and nutrient assays at three timepoints over a growing season (2021) in the Maumee Rivermouth (Toledo, OH). These data can be used along to estimate nutrient processing rates within the Maumee Rivermouth.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.089
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.012

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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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