Bibliographic record
Abstract
This version has been improved with a handful of modelling features, new algorithms, quality checks on inputs, and minor bug fixes. Notably, the following features have been added: • New Models Added – full (level 1) emulation support for the HYMOD2 hydrological model (Roy et al., 2017). • Radiation/ET/Rain-snow partitioning Algorithms – added new algorithms for estimating PET, LW incoming radiation, and partitioning between rain and snow • Lake Freezing – simple treatment of lake freezing and snow accumulation on frozen lakes with the :LakeFreeze command • Improved Stream Temperature simulation – better handling of longwave radiation, support for sensible heat exchange and groundwater mixing during in-catchment routing , support for gridded rainfall temperature inputs, • Basic Model Interface (BMI) interoperability – Raven can now be compiled as a linked library and interface directly with other BMI-compliant applications • Other – correction factors for wind speed and relative humidity; Spearman ranked correlation coefficient diagnostic, improved support for EnKF in a FEWS environment, improved handling of orographic corrections when using gridded precipitation/temperature data, temperature bias correction, writing of reservoir mass balance file in netCDF format, some previously hard-coded parameters now exposed to users; support for multiple water demands from a single subbasin • Notable Bug Fixes – repairs to handling of daily-averaged PET estimates, default estimation of longwave radiation, handling lake evaporation when :HRUID not supplied to reservoir, fix to rainfall on reservoirs when :LakeStorage is something other than SURFACE_WATER, repair of reservoir stage assimilation via direct insertion, fixes of netCDF issues when elevation attributes are provided • Improvements/updates to the Raven documentation and to Raven input quality checking.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.153 | 0.072 |
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".