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Record W6931625459 · doi:10.5281/zenodo.8136577

PFT-Vcmax25 optimization in CLASSIC (v.1.3)

2023· other· en· W6931625459 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeother
Languageen
FieldComputer Science
TopicImbalanced Data Classification Techniques
Canadian institutionsWilfrid Laurier UniversityEnvironment and Climate Change CanadaUniversité du Québec à Trois-RivièresUniversité de Montréal
Fundersnot available
KeywordsBenchmarkingLicenseCode (set theory)CovarianceGovernment (linguistics)Source codeOpen sourceCode of practice

Abstract

fetched live from OpenAlex

Here are the source code of the model and the data used in the project 'Optimization of maximum carboxylation rate in CLASSIC (v.1.3) for North America's boreal forests using eddy covariance observations'. The CLASSIC model is available from the project website: https://gitlab.com/cccma/classic under the Open Government License - Canada and the GNU General Public License version 2. The model benchmarking dataset used to evaluate the model performance can be freely obtained on Zenodo (https://doi.org/10.5281/zenodo.7266010; Qu et al., 2022).

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)0.004
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: Software · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0430.038

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 head0.039
GPT teacher head0.259
Teacher spread0.221 · 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
GenreSoftware

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 routes2
Has abstractyes

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