Bibliographic record
Abstract
ARC-Lake v2.0 - Per-Lake contains data products on a lake-by-lake basis. These data products contain observations of Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) from the series of (Advanced) Along-Track Scanning Radiometers ((A)ATSRs). ARC-Lake v2.0 data products cover the period from 1st August 1991 to 31st December 2011. A number of different data products are available for each lake and are grouped together into a zip archive for each lake. A summary of the types of data product available is given on http://datashare.is.ed.ac.uk/handle/10283/88 and full details of the file naming convention and file contents are given in the ARC-Lake Data Product Description document (ARCLake_DPD_v1_1_2.pdf). Individual lake archives are grouped into larger zip archives by continent (with the exception of the Caspian Sea). Details of the methods used and a list of all lakes and their locations are given in the ARC-Lake Algorithm Theoretical Basis Document (ARC-Lake-ATBD-v1.3.pdf). Additional information about the ARC-Lake project and some basic data analysis tools can be found on the project website: http://www.geos.ed.ac.uk/arclake Please cite both this dataset and the related publication: * "MacCallum, Stuart N; Merchant, Christopher J. (2013). ARC-Lake v2.0 - Per-Lake, 1991-2011 [Dataset]. University of Edinburgh. School of GeoSciences / European Space Agency. https://doi.org/10.7488/ds/161." * "MacCallum, S.N. and Merchant, C.J. (2012) Surface water temperature observations of large lakes by optimal estimation. Canadian Journal of Remote Sensing, 38 (1). pp. 25-45. ISSN 1712-7971 doi: 10.5589/m12-010"
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.252 | 0.342 |
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".