3 Year Greenhouse Gas data (collected in field)
Bibliographic record
Abstract
Read me: Publication: Soil greenhouse gas emissions and grazing management in northern temperate grasslands Contact Details: Please contact Ma Zilong at mazlong@mail.sysu.edu.cn and Bharat Shrestha at shresthabm@gmail.com if you have any further questions Methods: Growing season GHG fluxes were measured in the field bi-weekly from mid-August to mid-October in 2017, early May to mid-October in 2018, and early May to early September in 2019 using dark static chambers (65.5 × 17 × 15.5 cm height) at six random sampling points within a representative area of each study ranch. Ambient air samples(20 mL) were collected prior to placing the chamber-lids over the static chambers (ambient condition, t = 0) and again at 10, 20, and 30 min after placing the chamber-lid over the static chamber, using an airtight syringe (Norm-Ject, Henke Sass Wolf, Tuttlingen, Germany) through a rubber septum. Samples were then stored in pre-evacuated 12 mL soda glass Isomass Exetainers (Labco Limited, Lampeter, Wales, UK) to provide a positive pressure in the Exetainer. Collected gas samples were transported in dark boxes to the Forest Soil Laboratory at the University of Alberta, Edmonton, and were analyzed using a Varian CP-3800 gas chromatograph (Varian Canada, Mississauga, Canada) equipped with a thermal conductivity detector, a flame ionization detector and an electron capture detector for determining the CO2, CH4, and N2O concentrations, respectively.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".