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Record W6930608360 · doi:10.5281/zenodo.13833176

Novel Endpoint Characterization Factors for Life Cycle Impact Assessment of Terrestrial Acidification

2024· dataset· en· W6930608360 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEcoregionDeposition (geology)Normalization (sociology)Acid depositionLife-cycle assessmentTerrestrial ecosystemOffset (computer science)

Abstract

fetched live from OpenAlex

This repository contains Excel files with the characterization factorsand the soil response factors for the publication entitled "Novel Endpoint Characterization Factors for Life Cycle Impact Assessment of Terrestrial Acidification", published in the "Journal of Ecological Indicators" . Content: Datasets.zip is an folder containing the following Excel files: CF_Terrestrial_Acidification_2024-08-01.xlsx with the following sheets Dataframe gathering terrestrial acidification marginal endpoint CF [PDF.yr/kg_emitted] values at country level (with the world average value), for 3 acidifying substances (NOx, NHx, SOx) at global and regional impact scales (with and without the inclusion of the Global Extinction Probability - GEP - respectively) Calc Info summing up calculation informations Calc Table registering input parameters used to run the calculations leading to the Dataframe sheet (substance used, original and adapted resolutions for emission and deposition compartments, resolutions for each CF components and spatial transformations, GEP normalization method) RF_NO3_2024-03-27.xlsx with the following sheet: RFs containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in NO3 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs) RF_NH4_2024-03-25.xlsx with the following sheet: RFs containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in NH4 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs) RF_SO4_2024-03-25.xlsx with the following sheet: RFs containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in SO4 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs)

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)0.005
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.046
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0460.012

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 head0.036
GPT teacher head0.305
Teacher spread0.269 · 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
Published2024
Admission routes1
Has abstractyes

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