Deliverable 3.9 First implementation and data: Atmosphere and land
Bibliographic record
Abstract
This document reports the activities of the Task 3.5, Deliverable 3.9 “First implementation of the observing system: data delivery and report on results of the distributed observing systems for atmosphere and land”. D3.9 focuses on (a) atmospheric observation of the main greenhouse gases (CO2and CH4) using ground, mobile and airborne eddy covariance observations to determine the atmospheric concentrations and fluxes in North slope of Alaska and Sweden; (b) trace gases monitoring of N2O, SF6, CO, O2/N2 using a flask sampler for the automated collection of air samples under standardized conditions; (c) effects of snow cover on surface energy balance and permafrost thermal regime; (d) observation with a spectro-albedometer at high temporal resolution, and VNA-based radar system to monitor soil, snow and surface vegetation proper-ties;(e)multiple airborne campaigns conducted in the Alaskan North Slope,Mackenzie River Delta, Canada,and the Lena River Delta, Siberia,assessing the composition and height of the atmospheric boundary layer as well as greenhouse gas concentrations; (e) development of a low-maintenance atmospheric observatory onboard of the Swedish research icebreaker Oden that include measurements of incoming broad-band radiation, surface temperature, cloud-base lidars and eddy covariance fluxes of CO2and CH4 Referenced materials and products in this report were compiled with inputs from the INTAROS partners of Task 3.5
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.235 |
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".