MétaCan
Menu
← Back to cohort
Record W4394044580 · doi:10.5281/zenodo.7447396

"Four decades increase in gross photosynthesis of boreal forests balanced out by increase in ecosystem respiration" data and codes

2022· dataset· en· W4394044580 on OpenAlexaff
Jouni Pulliainen, Mika Aurela, Timo Vesala, Oliver Sonnentag, Anders Lindroth, Tuula Aalto, Tiina Markkanen, Juha Lemmetyinen, Tea Thum, Chris Derksen, Samuli Launiainen, Matias Takala, Juval Cohen, Miia Salminen, Hannakaisa Lindqvist, Kristin Böttcher, Kimmo Rautiainen, Kari Luojus, Jukka Pumpanen, Martin Heimann, Manuel Helbig, Matthias Peichl, Steffen M. Noe, Alisa Krasnova, Ivan Mammarella, Annalea Lohila, Anna Kontu, Elma Nevala, Pasi Kolari

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsDalhousie UniversityEnvironment and Climate Change CanadaUniversité de Montréal
Fundersnot available
KeywordsPhotosynthesisEcosystemRespirationEcosystem respirationTaigaEnvironmental scienceBoreal ecosystemBorealAtmospheric sciencesEcologyBiologyBotanyPrimary productionPhysics

Abstract

fetched live from OpenAlex

Data and codes related to the manuscript "Four decades increase in gross photosynthesis of boreal forests balanced out by increase in ecosystem respiration".

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 score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0380.032

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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicAtmospheric and Environmental Gas Dynamics→French-language works237,207→