Cycles in hydrologic intensification and de-intensification create instabilities in spring nitrate-N export C-Q behavior in northern temperate forests
Bibliographic record
Abstract
Data supporting analysis in "Cycles in hydrologic intensification and de-intensification create instabilities in spring nitrate-N export C-Q behavior in northern temperate forests". The worksheet "daily SWE" contains modeled daily snow water equivalent (SWE; mm) at Turkey Lakes Watershed. Daily values were modeled using a snow accumulation and melt routine within a hydrologic model that used measured precipitation and snow survey data as input and was calibrated to daily discharge measurements at a reference catchment. For use of SWE data, please cite:Leach, J. A., Buttle, J. M., Webster, K. L., Hazlett, P. W., & Jeffries, D. S. (2020). Travel times for snowmelt-dominated headwater catchments: influences of wetlands and forest harvesting, and linkages to stream water quality. Hydrological Processes, 34(10), 2154-2175. https://doi.org/10.1002/hyp.13746 The worksheet "monthly T, P, PET" contains monthly mean temperature (°C), monthly total precipitation (mm), and monthly Hamon potential evapotranspiration (PET; mm) from measurements made at the Algoma CAPMoN (Canadian Air and Precipitation Monitoring Network) meteorological station (47°02′N, 84°22′W, 411 m.a.s.l.). For use of these climate data, please cite:Semkin, R. G., Jeffries, D. S., Neureuther, R., Lahaie, G., McAulay, M., Norouzian, F., & Franklyn, J. (2012). Summary of hydrological and meteorological measurements in the Turkey Lakes Watershed, Algoma, Ontario, 1980-2010. Water Science and Technology Directorate Contribution No. 11-145. Environment Canada, National Water Research Institute, Burlington, ON, 85 p.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".