MétaCan
Menu
Back to cohort
Record W2782512382 · doi:10.1016/j.dib.2017.12.052

Spatially and temporally continuous estimates of annual total nitrogen deposition over North America, 1860–2013

2018· article· en· W2782512382 on OpenAlexafffundabout
Robbie A. Hember

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMinistry of Forests
FundersPacific Institute for Climate SolutionsU.S. Department of Agriculture
KeywordsDeposition (geology)NitrogenEnvironmental scienceGeographyBiologyChemistryPaleontology

Abstract

fetched live from OpenAlex

This report describes the North American Climate Integration and Diagnostics - Nitrogen Deposition Version 1 (NACID-NDEP1) database. The database contains estimates of annual total nitrogen (N) deposition for the purpose of supporting terrestrial ecosystem modelling in North America. It was constructed at 1-km resolution with coverage of Alaska, Canada, and the conterminous U.S., with continuous annual coverage from 1860 to 2013. Estimates were produced by acquiring and compiling best-available data sources: Wet N deposition was estimated from interpolation of monthly ammonium and nitrate concentration measurements and from grids of monthly precipitation. Dry N deposition was estimated from satellite measurements of ammonium and nitrogen oxides. Total N deposition for the pre-industrial era was derived from previous modelling studies. As these source datasets covered different time periods, several assumptions were required to produce a continuous record.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.838
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.213
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations29
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueData in BriefSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207