Yukon River discharge  temperature from 2017 – 2020 may be influenced by late summer headwaters flux and water vapor forcing, rather than decadal scale air temperature patterns over this time period
Bibliographic record
Abstract
The river mouth of the Yukon is home to subsistence fisheries which will become increasingly subject to changes in temperature and river chemistry. Predicting these changes will become important to scientists and stakeholders interested in preserving biodiversity[1]. The data in this study include the Group for High Resolution Sea Surface Temperature level-4 gridded 0.09 degree sea surface temperature dataset [2], The Arctic Great Rivers Yukon River volumetric flux dataset [3], the in situ VonFinster headwaters dataset [4], MERRA-2 air temperature [5], MERRA-2 water vapor [6]. A prior study illustrated that SST patterns in the Norton Sound followed decadal scale temperature in August Pacific Decadal Oscillation (PDO) indices from both the University of Washington and University of Tokyo timeseries [7; 8; 9]. This study also suggested that Yukon river SST closely followed air temperature over the river mouth from 2003 – 2020, but that river mouth temperature diverged from air temperature in the years 2017 – 2020. Yukon headwaters flux increased on or following September 1 in the years 2015 – 2019 during warm-summer PDO indices in the Norton Sound. This suggested an effect not directly related to air temperature over the river mouth. Additionally, three maximum Yukon river discharge years over 2003 – 2020 occurred during the warm-summer PDO years in the years 2005, 2016, and 2020. The year 2016, a large sum river discharge year, also had positive water vapor forcing, suggesting that atmospheric effects may be related to extra river discharge during this time frame. References: [1]Warkentin, L.; Parken, C. K.; Bailey, R.; Moore, J. W. Low summer river flows associated with low productivity of chinook salmon in a watershed with shifting hydrology. Ecol. Solute. Evid. 2022, 3, e12124 doi: 10.1002/2688-8319.12124. [2] Remote Sensing Systems (REMSS). GHRSST Level 4 MW_IR_OI Global Foundation Sea Surface Temperature analysis version 5.0 from REMSS (GDS versions 1 and 2), [2003-2020, 165° W-180° W, 52° N-70° N]. NOAA National Centers for Environmental Information, 2008.{Accessed: September, 2002} [3]Shiklomanov, A.I.; Holmes R.M.; McClelland J.W.; Tank S.E.; Spencer, R.G.M. Arctic Great Rivers Observatory, Discharge Dataset (2021, September 8), 0214. Retrieved from https://www.arcticrivers.org/data. [4]von Finster, A.; Folkes, M. Yukon River water temperature data series (2021, March 6), Pacific Salmon Commission. https://www.psc.org [5]Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 surface air temperature, instM_2d_lfo_Nx.200301-20201231: 2d, Monthly mean,Time-Averaged, Single-Level Assimilation, Single-Level Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC), [08/09/2023], doi: 10.5067/AP1B0BA5PD2K. [6]Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 water vapor, instM_2d_lfo_Nx.200301-20201231: 2d, Monthly mean,Time-Averaged, Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC), [Data 08/09/2023], 10.5067/AP1B0BA5PD2K. [7] Spratt, R., Vazquez, J., Menemenlis, D., Carroll, D. A Synoptic Scale Comparison of Yukon River Mouth Temperature to Open Source Modeled, in-situ, and reanalysis data from 2003-2020, 2024, IEEE, in-review. [8]University of Tokyo. (2006, December 7). Tokyo Climate Center website and its products - data.jma.go.jp., Tokyo Climate Center Library. Retrieved from https://ds.data.jma.go.jp/tcc/tcc/index.html. [9]Smith, T. M., Reynolds, R. W., Peterson, T. C., and Lawrimore, J. Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880-2006), J. Climate, 2008, 21( 10), 2283 – 2296.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".