Limnology and Oceanography: Methods最新文献_第7页

A low-cost, automated, and adaptable system for field-based measurement of pCO2, pCH4, and associated variables in groundwater 一种低成本、自动化和适应性强的系统，用于地下水中二氧化碳、甲烷和相关变量的现场测量

IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-02-13 Epub Date: 2025-11-19 DOI: 10.1002/lom3.70012

Madeline McChesney Every, Jesse T. Dugan, John D. Kessler

{"title":"A low-cost, automated, and adaptable system for field-based measurement of pCO2, pCH4, and associated variables in groundwater","authors":"Madeline McChesney Every, Jesse T. Dugan, John D. Kessler","doi":"10.1002/lom3.70012","DOIUrl":"10.1002/lom3.70012","url":null,"abstract":"The Groundwater Automated Chemical Examiner (GrACE) is an automated analytical system of integrated sensors for the field-based measurement of chemical and physical properties of groundwater. The GrACE system was designed to explore parameters influencing the dissolved concentrations of carbon dioxide (pCO2) and methane (pCH4) in groundwater, incorporating one of two equilibrator designs to measure dissolved gas partial pressures. The pCO2 sensor has an operating range of 200–100,000 μatm with an accuracy and precision of 17% and 27.5%, respectively, while the pCH4 sensor has a range of 800–8000 μatm and an accuracy and precision of 3% and 3.2%, respectively, over a month-long measurement campaign without recalibration. The e-folding response times to changes in pCO2 and pCH4 are 41.7–50.3 and 103–173 s, respectively. Field trials using the GrACE system found fluctuations in CO2 and CH4 concentrations of over 1270 and 12 μmol L−1, respectively, in a 24-h period strongly correlated with variability in groundwater volume. These results reveal that the response times of the analytical system are > 30 times shorter than short-term environmental variations measured here. Complementary measurements of water temperature, electrical conductivity (EC), pH, turbidity, water depth, and dissolved oxygen (pO2) are also conducted by the GrACE system. The GrACE system uses open-source code and relatively low-cost, low-power 5 V sensors, several of which are “plug-and-play,” enabling (i) modifications to the design presented here for changing experimental conditions and (ii) deployments in remote locations lasting weeks to months using solar powered batteries so both short-term (hours to days) and longer-term (seasonal) processes can be explored.","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"24 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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What is “accuracy”? Rethinking machine learning classifier performance metrics for highly imbalanced, high variance, zero-inflated species count data 什么是“准确性”？重新思考高度不平衡、高方差、零膨胀物种计数数据的机器学习分类器性能指标

IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-02-13 Epub Date: 2025-11-15 DOI: 10.1002/lom3.70009

Bianca M. Owen, James R. Tweedley, Navid R. Moheimani, Chris S. Hallett, Jeffrey J. Cosgrove, Leopold P. O. Silberstein

{"title":"What is “accuracy”? Rethinking machine learning classifier performance metrics for highly imbalanced, high variance, zero-inflated species count data","authors":"Bianca M. Owen, James R. Tweedley, Navid R. Moheimani, Chris S. Hallett, Jeffrey J. Cosgrove, Leopold P. O. Silberstein","doi":"10.1002/lom3.70009","DOIUrl":"10.1002/lom3.70009","url":null,"abstract":"Machine learning has opened the door for the automated sorting (classification) of images, holograms and acoustic backscatters of individual plankton, invertebrates, fish and marine mammals. However, this field is complicated by decades of paradoxically promising reports of classifier performance that do not correlate with real-world uptake of this technology in aquatic sciences. Simple metrics of classifier performance are essential for optimizing, evaluating and comparing machine learning classifiers, but a wide variety of metrics and calculation variants have been proposed. Several characteristics of species count data influence metric behavior: severe imbalance and variance, zero-inflation, high class numbers and contamination with non-target classes. This study explores the hidden complexity of classifier performance metrics for species count data using synthetic datasets and simulated classifier outputs. It demonstrates how these data characteristics can severely distort metric values, with seven of eight variants of the most common metric, Accuracy, returning near-perfect scores (up to 98%) even when no instances are correctly classified. Clear recommendations are made for classifier evaluation pitfalls and metric variants to avoid, ultimately finding one variant of the F1-Score (mF1) to be the most suitable single metric, with several important calculation caveats specific to species count data. Due to ambiguous terminology and inconsistent definitions, it is often impossible to identify which variant of a performance metric has been applied in classifier studies. It is vital that authors are intentional and transparent about their metric use to support the vast potential for machine learning to revolutionize the research and monitoring of aquatic environments.","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"24 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-02-13

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Using physics-informed neural networks to quantify submarine groundwater discharge under high-frequency tidal dynamics using heat as a tracer 利用物理信息神经网络，以热量作为示踪剂，量化高频潮汐动力学下的海底地下水排放

IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-02-13 Epub Date: 2025-11-19 DOI: 10.1002/lom3.70015

S. Frei, M. U. Munir, A. M. Seiverth, B. S. Gilfedder

{"title":"Using physics-informed neural networks to quantify submarine groundwater discharge under high-frequency tidal dynamics using heat as a tracer","authors":"S. Frei, M. U. Munir, A. M. Seiverth, B. S. Gilfedder","doi":"10.1002/lom3.70015","DOIUrl":"10.1002/lom3.70015","url":null,"abstract":"Estimating exchange rates of submarine groundwater discharge (SGD) at high temporal resolution over extended periods remains challenging, particularly when using heat as a tracer in highly dynamic environments such as tidal systems. Currently available heat transport models struggle to accurately quantify SGD exchange rates in these settings, where sharp transitions and high-frequency fluctuations in hydrodynamic forcings cannot be reproduced with current inverse optimization methods. This study evaluates the ability of Physics-Informed Neural Networks (PINNs) to overcome these limitations through inverse modeling of temperature derived Darcy fluxes. Three case studies—a synthetic step-function transition, a synthetic tidal pumping scenario, both published datasets, and measurements from a field-scale tidal creek (Texel, Netherlands)—were used to test and validate the PINN approach. Unlike conventional methods such as VFLUX and 1DTEMPRO, which exhibited delays and inaccuracies when tidal cycles influence groundwater fluxes on sub-daily to daily frequencies, the PINN successfully predicted exchange fluxes driven by the K1 (24-h) and M2 (12-h) tidal cycles with a high degree of accuracy. Spectral analysis confirmed the PINNs ability to precisely estimate the impact of multi-scale tidal signals on SGD dynamics at a sub-daily resolution. This work highlight PINN as a powerful inversion method when using heat as a tracer in highly dynamic environments, offering new insights into the complex interplay between tidal signals and SGD in coastal systems.","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"24 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146256502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-02-07

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IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-02-03

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IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-01-31

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IF 1.9 3区地球科学

Limnology and Oceanography: Methods Pub Date : 2026-01-22