Measured and modeled methane concentration and diffusive methane fluxes at Lake Janauacá (Brazil) between February 2015 and August 2016.
Bibliographic record
Abstract
The dataset contains 14 Excel files of data corresponding to Figures 3 to 6 and Figures S1 to S10 in the support information in ‘Linking biogeochemical and hydrodynamic processes to model methane fluxes in shallow, tropical floodplain lakes’. The dataset covers measured data, simulation results by the Arctic Lake Biogeochemical Model (ALBM) and simulation results by the 3-Dimensional coupled Hydrodynamic-Aquatic Ecosystem Model (AEM3D) during the simulation periods in the manuscript. The AEM3D results were prepared as part of the input data of ALBM. The dataset includes: 1. Profiles of dissolved oxygen concentrations measured by moored sensors every 10 minutes and corresponding ALBM simulated profiles of dissolved oxygen concentrations. 2. Methane concentrations sampled manually at intermittent times during each simulation period, and corresponding ALBM simulated methane concentrations. 3. ALBM simulated methane concentrations at 5-minute intervals.4. Diffusive methane fluxes measured by chambers collecting methane gas during the period of deployment and expressed as total emitted per hour, and ALBM simulated diffusive methane fluxes computed as the total flux per hour and assigned to the time at the end of each hour. 5. Measured and AEM3D simulated temperature profiles averaged to 5-minute intervals.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".