Data associated with article "Bulk Transfer Coefficients Estimated from Eddy-Covariance Measurements Over Lakes and Reservoirs" by Guseva et al., 2022
Bibliographic record
Abstract
The data includes (a) the general information about the lakes and reservoirs under study (e.g., lake surface area, lake mean and maximum depth); (b) the publications and data repository references for each individual lake or reservoir where we took the original datasets from (for details, see the article); (c) the number of data points (for the estimated bulk transfer coefficients) and filters applied to each dataset. ('Table_Data_Bulk_Transfer_Coeff.docx') In addition, we attach the derived quantities for each lake and reservoir that we analyzed in our manuscript: the neutral bulk transfer coefficients of (a) momentum (the drag coefficient); (b) heat (the Stanton number); (c) water vapor (the Dalton number). ('Data_Bulk_Transfer_Coeff.xlsx') Update 22.11.2022: After the first round of revisions we upload the new version of the data since we had to recalculate the transfer coefficients. (1) We added the median values of the transfer coefficients; (2) we added the transfer coefficients accounting for gustiness. ('Data_Bulk_Transfer_Coeff.xlsx')
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.306 | 0.213 |
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".