Data on under-ice temperatures and solar radiation in Lake Ngoring (Qinghai-Tibet)
Bibliographic record
Abstract
Dataset description: <br> Data on under-ice temperatures and solar radiation were collected in Lake Ngoring in winter 2015/2016 in a field experiment on under-ice mixing and radiation regime of Tibetan lakes. See Kirillin et al. (submitted to Geophysical Research Letters 2021) for the details on the experiment. _Metadata_ Study site: Lake Ngoring (35° 02.65'N, 97° 42.23'E)<br> Observation period: 01 Nov 2015 - 01 Jun 2016 -- DATASET 1 Data type: Time series of water temperature [°C] at different depths. Probes: <br> T-Solo (RBR Canada) Pre-processing: data sampled at 10 s rate and averaged over 30 min intervals. data file(s): <br> TibetTemp.csv data format: comma separated variables<br> data table contains temperature values (one column per measurement depth) except<br> 1st column: UTC Date and Time [dd-mmm-yyyy HH:MM]<br> 1st row: Depth from the surface [metres] ----<br> DATASET 2 Data type: quantum irradiance [μmol s^-1 m^-2] at 2.4 m and 3.6 m depth from the lake surface probe(s): JFE Advantech DEFI2-L cosine corrected PAR radiation logger (JFE Advantech, Japan) Pre-processing: data sampled at 10 min rate and averaged over 30 min intervals. data file(s): <br> TibetRad.csv data format: comma separated variables<br> 1st column: UTC Date and Time [dd-mmm-yyyy HH:MM]<br> 2nd column: quantum irradiance [μmol s^-1 m^-2] at 2.4 m depth<br> 3rd column: quantum irradiance [μmol s^-1 m^-2] at 3.6 m depth<br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.162 | 0.002 |
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; both teacher heads agree on what is shown here.
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".