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Record W4403796035 · doi:10.1594/pangaea.955032

Global coastal groundwater and subterranean estuary nutrients

2023· article· en· W4403796035 on OpenAlexaff
Stephanie J. Wilson, Amy Moody, Tristan McKenzie, M. Bayani Cardenas, Elco Luijendijk, Audrey H. Sawyer, Alicia M. Wilson, Holly A. Michael, Bochao Xu, Karen L. Knee, Hyung‐Mi Cho, Yishai Weinstein, Adina Paytan, Nils Moosdorf, Chen‐Tung Arthur Chen, Mélanie Beck, Cody V. Lopez, Dorina Murgulet, Guebuem Kim, Matt Charette, Hannelore Waska, J. Severino P. Ibánhez, Gwénaëlle Chaillou, Till Oehler, Shin‐ichi Onodera, Mitsuyo Saito, Valentí Rodellas, Natasha Dimova, Daniel Montiel, Henrietta Dulai, Jinzhou Du, Eric Petermann, Xiaogang Chen, Kay L. Davis, Sébastien Lamontagne, Ryo Sugimoto, Guizhi Wang, Américo Iadran Torres, Cansu Demir, Emily Bristol, Craig T. Connolly, Brenno Januario da Silva, Douglas R. Tait, Bsk Kumar, R. Viswanadham, V. S. Sarma, Emmanoel Vieira Silva-Filho, Alan M. Shiller, Alanna L Lecher, Henry Bokuniewicz, Carlos Rocha, Anja Reckhardt, Michael E. Böttcher, Shan Jiang, Thomas Stieglitz, Céline Charbonnier, Pierre Anschutz, Laura Hernández‐Terrones, S. Suresh Babu, Beata Szymczycha, Mahmood Sadat‐Noori, Luís Felipe Hax Niencheski, K. A. Null, Craig Tobias, Bongkeun Song, Iris C. Anderson, Isaac R. Santos

Bibliographic record

VenueDIGITAL.CSIC (Spanish National Research Council (CSIC)) · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversité du Québec à Rimouski
FundersAustralian Research CouncilVetenskapsrådetKnut och Alice Wallenbergs StiftelseNational Science Foundation
KeywordsEstuaryNutrientGroundwaterEnvironmental scienceHydrology (agriculture)OceanographyGeographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

These data were compiled from original and published datasets of coastal groundwater / subterranean estuary research efforts along global coastline (sites within 1km of shoreline). The dataset includes sampling site names, locations, original sample information, sample depth, temperature, salinity, dissolved nitrogen concentrations, and dissolved phosphorus concentrations. The data source or curator is also included in the dataset.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.014
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.007

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 head0.129
GPT teacher head0.300
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

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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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