Application of frequency-dependent nudging in biogeochemical modeling and assessment of marine animal tag data for ocean observations
Bibliographic record
Abstract
Numerical models are powerful and widely used tools for environmental prediction; however, any model prediction contains errors due to imperfect model parameterizations, insufficient model resolution, numerical errors, imperfect initial and boundary conditions etc. A variety of approaches is applied to quantify, correct and minimize these errors including skill assessments, bias correction and formal data assimilation. All of these require observations and benefit from comprehensive data sets. In this thesis, two aspects related to the quantification and correction of errors in biological ocean models are addressed: (i) A new bias correction method for a biological ocean model is evaluated, and (ii) a novel approach for expanding the set of typically available phytoplankton observations is\nassessed.\nThe bias correction method, referred to as frequency-dependent nudging, was proposed\nby Thompson et al. (Ocean Modelling, 2006, 13:109-125) and is used to nudge a model\nonly in prescribed frequencies. A desirable feature of this method is that it can preserve\nhigh frequency variability that would be dampened with conventional nudging. The method\nis first applied to an idealized signal consisting of a seasonal cycle and high frequency\nvariability. In this example, frequency-dependent nudging corrected for the imposed\nseasonal bias without affecting the high-frequency variability. The method is then applied\nto a non-linear, 1 dimensional (1D) biogeochemical ocean model. Results showed that\napplication of frequency-dependent nudging leads to better biogeochemical estimates than\nconventional nudging.\nIn order to expand the set of available phytoplankton observations, light measurements\nfrom sensors attached on grey seals where assessed to determine if they provide a useful\nproxy of phytoplankton biomass. A controlled experiment at Bedford Basin showed\nthat attenuation coefficient estimates from light attenuation measurements from seal tags\nwere found to correlate significantly with chlorophyll. On the Scotian Shelf, results of\nthe assessment indicate that seal tags can uncover spatio-temporal patterns related to\nphytoplankton biomass; however, more research is needed to derive absolute biomass\nestimates in the region.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".