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Record W4206377022 · doi:10.2737/rds-2021-0045

Global peatland Fourier Transform Infrared Spectroscopy (FTIR) infrared values

2021· dataset· en· W4206377022 on OpenAlexaff
B. A. Verbeke, Louis J. Lamit, Erik A. Lilleskov, Suzanne B. Hodgkins, Nathan Basiliko, Evan S. Kane, Roxane Andersen, Rebekka Artz, Juan C. Benavides, Brian W. Benscoter, Werner Borken, Luca Bragazza, Stefani M. Brandt, Suzanna L. Bräuer, Michael A. Carson, Dan J. Charman, Xin Chen, Beverley R. Clarkson, Ale×ander R. Cobb, Peter Convey, Jhon del Águila Pasquel, Andrea Soledad Enriquez, Howard Griffiths, Samantha Grover, Charles F. Harvey, Lorna I. Harris, Christina Hazard, Dominic A. Hodgson, Alison M. Hoyt, John A. Hribljan, Jyrki Jauhiainen, Sari Juutinen, Klaus‐Holger Knorr, Randall K. Kolka, Mari Könönen, Tuula Larmola, Carmondy K. McCalley, James W. McLaughlin, Tim R. Moore, Nadia Mykytczuk, Anna E. Normand, Virginia I. Rich, Nigel T. Roulet, Jessica Royles, Jasmine Rutherford, David Stanley Smith, Mette M. Svenning, Leho Tedersoo, Phạm Quang Thu, Carl Trettin, Eeva‐Stiina Tuittila, Zuzana Urbanová, R. K. Varner, Meng Wang, Zheng Wang, Matthew Warren, Magdalena M. Wiedermann, Shanay Williams, Joseph B. Yavitt, Zhi‐Guo Yu, Zicheng Yu, Jeffrey P. Chanton

Bibliographic record

VenueForest Service Research Data Archive · 2021
Typedataset
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill UniversityOntario Forest Research InstituteUniversity of SaskatchewanUniversity of AlbertaLaurentian University
FundersNorthern Research StationU.S. Department of EnergyNational Science Foundation
KeywordsFourier transform infrared spectroscopyInfraredPeatFourier transformInfrared spectroscopyRange (aeronautics)Resource (disambiguation)Fourier transform spectroscopySpectroscopyEnvironmental scienceAnalytical Chemistry (journal)Remote sensingPhysicsMaterials scienceComputer scienceGeographyChemistryOpticsEnvironmental chemistryAstronomy

Abstract

fetched live from OpenAlex

This archive contains research data collected and/or funded by Forest Service Research and Development (FS R&D), U.S. Department of Agriculture. It is a resource for accessing both short and long-term FS R&D research data, which includes Experimental Forest and Range data. It is a way to both preserve and share the quality science of our researchers.

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)0.002
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 score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.043

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 head0.045
GPT teacher head0.342
Teacher spread0.297 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueForest Service Research Data ArchiveSame topicPeatlands and Wetlands EcologyFrench-language works237,207