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- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 30 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
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- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 14 May 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 11 Jan 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 23 Jan 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 12 Feb 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 5 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 16 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 29 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 4 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 10 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 16 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 5 May 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 5 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 17 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 23 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 27 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- HTML
- data.gov.au
Quantifying social vulnerability: a methodology for identifying those at risk to natural hazards
Geoscience Australia DataUpdated 16 Oct 2025
Australia is exposed to a wide range of natural hazards, including earthquake, cyclone, landslide, flood, storm surge, severe …
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- data.gov.au
A national-scale vulnerability assessment of seawater intrusion: literature review, data review and method development
Geoscience Australia DataUpdated 16 Oct 2025
… This report is the literature review, data review and method development component of the project. It analyses existing …
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- Open Government Canada
Travel and border - COVID 19 post-advertising campaign evaluation tool (ACET) : methodological report
Global Affairs Canada | Affaires mondiales CanadaUpdated 6 Feb 2026
Archived and Outdated Information: This publication is out of date and should be referenced for research or recordkeeping …
- data.gov.au
Seasonal Dynamic Reference Cover Method - Landsat, JRSRP Algorithm Version 3.0, Queensland Coverage
Terrestrial Ecosystem Research Network's Data DiscoveryUpdated 19 Jun 2026
The seasonal dynamic reference cover method product compares the current ground cover level of each pixel to a …
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