Meteorological and thermal structure data at Lake Janauacá from November 2014 to September 2016
Bibliographic record
Abstract
This dataset contains meteorological data, measured and simulated temperature and buoyancy frequency data reported in the paper titled 'Hydrodynamic modeling of stratification and mixing in shallow, tropical floodplain lakes' by Zhou et al.. The meteorological data include time (Time) shortwave radiation (SWin), air temperature (Tair), relative humidity (RH), wind speed (WS), wind direction (WD) and rainfall (Rain) prepared for AEM3D and DYRESM simulations during the periods of field campaigns from November 2014 to September 2016 (Met_campaigns) and data for AEM3D simulations with simulation length extended (Met_extended.nc). Correspondingly, temperature and buoyancy frequency data have been organized into two files, one for the campaign periods (T_N_campaigns.nc) and the other for the extended periods (T_N_extended.nc). Each temperature and buoyancy frequency data file contains time (Time), depth (depth), temperature (T) and buoyancy frequency (N), with measured data marked with 'measured' and simulated data marked with 'aem3d' or 'dyresm'. These data have been directly used to create Fig. 3, 4, 7, 11 in the paper and Fig. S1, S2, S5, S7, S9, S11 and S14 to S17 in the supporting information.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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".