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license: cc-by-sa-4.0 |
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language: |
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- en |
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# phytoClassUCSC |
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Sections and prompts from the [model cards paper](https://arxiv.org/abs/1810.03993), v2. |
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Jump to section: |
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- [Model details](#model-details) |
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- [Intended use](#intended-use) |
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- [Factors](#factors) |
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- [Metrics](#metrics) |
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- [Evaluation data](#evaluation-data) |
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- [Training data](#training-data) |
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- [Quantitative analyses](#quantitative-analyses) |
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- [Ethical considerations](#ethical-considerations) |
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- [Caveats and recommendations](#caveats-and-recommendations) |
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## Model details |
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Review section 4.1 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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- Developed by the Kudela Lab from the Ocean Sciences Department at University of California, Santa Cruz. |
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- Current version trained in February, 2023. |
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- phytoClassUCSC-SoftNone02162023 |
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- phytoClassUCSC is a depthwise- CNN based on the Xception architecture [Chollet, F., 2017](https://arxiv.org/abs/1610.02357) with 134 layers using weights pretrained on ImageNet. |
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- An average pooling layer is used. |
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- Paper or other resource for more information |
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- Citation details |
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- License |
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- Email Patrick Daniel ([[email protected]]([email protected])) for questions |
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## Intended use |
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_Use cases that were envisioned during development._ |
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Review section 4.2 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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### Primary intended uses |
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### Primary intended users |
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### Out-of-scope use cases |
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## Factors |
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_Factors could include demographic or phenotypic groups, environmental conditions, technical |
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attributes, or others listed in Section 4.3._ |
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Review section 4.3 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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### Relevant factors |
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### Evaluation factors |
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## Metrics |
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_The appropriate metrics to feature in a model card depend on the type of model that is being tested. |
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For example, classification systems in which the primary output is a class label differ significantly |
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from systems whose primary output is a score. In all cases, the reported metrics should be determined |
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based on the model’s structure and intended use._ |
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Review section 4.4 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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### Model performance measures |
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### Decision thresholds |
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### Approaches to uncertainty and variability |
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## Evaluation data |
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_All referenced datasets would ideally point to any set of documents that provide visibility into the |
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source and composition of the dataset. Evaluation datasets should include datasets that are publicly |
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available for third-party use. These could be existing datasets or new ones provided alongside the model |
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card analyses to enable further benchmarking._ |
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Review section 4.5 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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### Datasets |
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### Motivation |
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### Preprocessing |
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## Training data |
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Review section 4.6 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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## Quantitative analyses |
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_Quantitative analyses should be disaggregated, that is, broken down by the chosen factors. Quantitative |
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analyses should provide the results of evaluating the model according to the chosen metrics, providing |
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confidence interval values when possible._ |
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Review section 4.7 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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### Unitary results |
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### Intersectional result |
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## Ethical considerations |
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_This section is intended to demonstrate the ethical considerations that went into model development, |
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surfacing ethical challenges and solutions to stakeholders. Ethical analysis does not always lead to |
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precise solutions, but the process of ethical contemplation is worthwhile to inform on responsible |
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practices and next steps in future work._ |
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Review section 4.8 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
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### Data |
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### Human life |
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### Mitigations |
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### Risks and harms |
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### Use cases |
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## Caveats and recommendations |
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_This section should list additional concerns that were not covered in the previous sections._ |
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Review section 4.9 of the [model cards paper](https://arxiv.org/abs/1810.03993). |
