Modified half-hourly FLUXNET dataset for 10 Boreal forest sites (CA-Obs,CA-Ojp,CA-Qfo,FI-Hyy,FI-Ken,FI-Let,FI-Sod,RU-Fyo,RU-Zot,US-Prr)
Bibliographic record
Abstract
This set contains half-hourly driving data and observations used in the simulations described in gmd-2018-313 (doi:10.5194/gmd-2018-313). Originally, this data is part of the FLUXNET2015 dataset (doi:10.17616/R36K9X). We have quality checked and gap-filled this data to suit the simulations. The upload contains site specific csv-files, a data header that is common to all files and a README. The actual data contains half-hourly values for: gross primary production (GPP, mol m-2 s-1) evapotranspiration (ET, kg m-2 s-1) air temperature (air_temp, degrees celcius) air pressure (air_pressure, Pa) precipitation (precip, kg m-2 s-1) specific humidity (qair, kg kg-1) wind speed (wspeed, m s-1) CO2 concentration (CO2, mol mol-1) shortwave radiation (shortwave, W m-2) longwave radiation (longwave, W m-2) potential shortwave radiation (mpot, W m-2) The sites (named by their FLUXNET identifier) and the years of data in this set are: CA-Obs (Saskatchewan) 1999-2006 CA-Ojp (Saskatchewan) 2004-2006 CA-Qfo (Quebec) 2003-2010 FI-Hyy (Hyytiälä) 1999-2006 FI-Ken (Kenttärova) 2003-2010 FI-Let (Lettosuo) 2010-2012 FI-Sod (Sodankylä) 2001-2008 RU-Fyo (Fyodorkovskoye) 2002-2009 RU-Zot (Zotino) 2002-2004 US-Prr (Poker Flat) 2011-2013
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".