Difference between revisions of "Statistical Manager Algorithms"
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The complete list of algorithms supported by the [[Statistical Manager]] service is reported below. | The complete list of algorithms supported by the [[Statistical Manager]] service is reported below. | ||
| − | Algorithms are clustered in the following categories: | + | Algorithms are clustered in the following categories: ... to be completed |
| − | + | * '''''[[#Clustering Algorithms | Clustering]]''''': ... | |
| − | * '''''Ecological Modeling''''' | + | * '''''[[#Ecological Modeling Algorithms | Ecological Modeling]]''''': ... |
: [[#AQUAMAPSNN | AQUAMAPSNN]], [[#AQUAMAPS_NATIVE | AQUAMAPS_NATIVE]], [[#AQUAMAPS_NATIVE_2050 | AQUAMAPS_NATIVE_2050]], [[#AQUAMAPS_NATIVE_NEURALNETWORK | AQUAMAPS_NATIVE_NEURALNETWORK]] | : [[#AQUAMAPSNN | AQUAMAPSNN]], [[#AQUAMAPS_NATIVE | AQUAMAPS_NATIVE]], [[#AQUAMAPS_NATIVE_2050 | AQUAMAPS_NATIVE_2050]], [[#AQUAMAPS_NATIVE_NEURALNETWORK | AQUAMAPS_NATIVE_NEURALNETWORK]] | ||
| − | * '''''Miscellaneous:''''' algorithms do not belonging to any of the above categories; | + | * '''''[[#Miscellaneous Algorithms | Miscellaneos]]:''''' algorithms do not belonging to any of the above categories; |
: [[#ABSENCE_CELLS_FROM_AQUAMAPS | ABSENCE_CELLS_FROM_AQUAMAPS]], [[#TIMEEXTRACTION | TIMEEXTRACTION]], [[#ZETAEXTRACTION_TABLE | ZETAEXTRACTION_TABLE]] | : [[#ABSENCE_CELLS_FROM_AQUAMAPS | ABSENCE_CELLS_FROM_AQUAMAPS]], [[#TIMEEXTRACTION | TIMEEXTRACTION]], [[#ZETAEXTRACTION_TABLE | ZETAEXTRACTION_TABLE]] | ||
| − | == Clustering == | + | == Clustering Algorithms == |
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| − | == Ecological Modeling == | + | == Ecological Modeling Algorithms == |
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|} | |} | ||
| − | == Signal Processing == | + | == Signal Processing Algorithms == |
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|} | |} | ||
| − | == Miscellaneous == | + | == Miscellaneous Algorithms == |
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Revision as of 19:44, 13 March 2014
The complete list of algorithms supported by the Statistical Manager service is reported below.
Algorithms are clustered in the following categories: ... to be completed
- Clustering: ...
- Ecological Modeling: ...
- Miscellaneos: algorithms do not belonging to any of the above categories;
Clustering Algorithms
| xxx
| |
|---|---|
| Description | ... |
| Type | ... |
| Execution | ... |
Ecological Modeling Algorithms
| AQUAMAPSNN
| |
|---|---|
| Description | The AquaMaps model trained using a Feed Forward Neural Network. This is a method to train a generic Feed Forward Artifical Neural Network to be used by the AquaMaps Neural Network algorithm. Produces a trained neural network in the form of a compiled file which can be used later. |
| A <type> algorithm that <what it does>. It accepts as input <input>. It produces <output>. <limitation>. For more information see: <citation/ref> | |
| Type | Models |
| Execution | ... |
| AQUAMAPS_NATIVE
| |
| Description | Algorithm for Native Distribution by AquaMaps. A distribution algorithm that generates a table containing species distribution probabilities on half-degree cells according to the AquaMaps approach for Native (Actual) distributions. |
| A distribution algorithm that generates a table containing species distribution probabilities on half-degree cells according to the AquaMaps approach with suitable distribution. It accepts as input a table containing species envelops (HSPEN), a table containing environmental parameters (HCAF) and a table containing species occurrences points (half-degree cells). It produces a table containing species distribution probabilities. <limitation>. For more information see: Kesner-Reyes, K., K. Kaschner, S. Kullander, C. Garilao, J. Barile, and R. Froese. 2012. AquaMaps: algorithm and data sources for aquatic organisms. In: Froese, R. and D. Pauly. Editors. 2012. FishBase. World Wide Web electronic publication. www.fishbase.org, version (04/2012). | |
| Type | Distributions |
| Execution | ... |
| AQUAMAPS_NATIVE_2050
| |
| Description | Algorithm for Native 2050 Distribution by AquaMaps. A distribution algorithm that generates a table containing species distribution probabilities on half-degree cells according to the AquaMaps approach with native distribution estimated for 2050. |
| Type | Distributions |
| Execution | ... |
| AQUAMAPS_NATIVE_NEURALNETWORK
| |
| Description | Aquamaps Native Algorithm calculated by a Neural Network. A distribution algorithm that relies on Neural Networks and AquaMaps data for native distributions to generate a table containing species distribution probabilities on half-degree cells. |
| Type | Distributions |
| Execution | ... |
| xxx
| |
| Description | ... |
| Type | ... |
| Execution | ... |
Signal Processing Algorithms
| xxx
| |
|---|---|
| Description | ... |
| Type | ... |
| Execution | ... |
Miscellaneous Algorithms
| ABSENCE_CELLS_FROM_AQUAMAPS
| |
|---|---|
| Description | An algorithm producing cells and features (HCAF) for a species containing absence points taken by an Aquamaps Distribution. |
| A transducer algorithm that generates an Half-degree Cells Authority File (HCAF) dataset for species estimated absences points. It accepts as input a table xxx, a table xxx, the target species and the number of points to select. It produces an HCAF table containing environmental parameters on selected points. <limitation>. For more information see: Kesner-Reyes, K., K. Kaschner, S. Kullander, C. Garilao, J. Barile, and R. Froese. 2012. AquaMaps: algorithm and data sources for aquatic organisms. In: Froese, R. and D. Pauly. Editors. 2012. FishBase. World Wide Web electronic publication. www.fishbase.org, version (04/2012). | |
| Type | Transducer |
| Execution | Single machine |
| TIMEEXTRACTION
| |
| Description | An algorithm to extract a time series of values associated to a geospatial features repository (e.g. NETCDF, ASC, GeoTiff files etc. ). The algorithm analyses the time series and automatically searches for hidden periodicities. It produces one chart of the time series, one table containing the time series values and possibly the spectrogram. |
| Type | Transducer |
| Execution | Single machine |
| ZETAEXTRACTION_TABLE
| |
| Description | An algorithm to extract a time series of values associated to a table containing geospatial information. The algorithm analyses the time series and automatically searches for hidden periodicities. It produces one chart of the time series, one table containing the time series values and possibly the spectrogram. |
| Type | Transducer |
| Execution | Single machine |
