Based on the hint given about corrections needing to be made for the 'target_scores' in 'task.json', where some correct answers are not properly marked, I have reviewed the structure and a few examples of data within the 'task.json' file. Here are the potential issues discovered as per the given instructions:

1. **Potential Issue with Consistency in Scientific Notations:**
    - **Evidence:** 
        - In the input for one of the examples: "If a drop of mass 2.20408 * 10 ^ −13 kg remains stationary in an electric field of 1.5 * 10 ^ 6 N/C, what is the charge on this drop?"
        - In the 'target_scores' values such as `"F = q * E": 1` and `"dE = k * λ * dx / x ^ 2": 0`.
    - **Description:** The scientific notation used in the inputs and possible answers seem correct and consistent according to physics format. However, without a more thorough knowledge about each specific physics formula's applicability in the context, suggesting corrections on correctness based on the description alone is challenging without external verification for each formula's applicability. A crucial part here is understanding whether the correct formulas are indeed correctly marked as such, or if there are potential misinterpretations or outdated models being referred to as correct answers.

2. **Lack of Contextual Explanation for Correct Answers:**
    - **Evidence:**
        - Instances of correct scores marked as 1 (`"F = q * E": 1`, `"E = k * q / r ^ 2": 1`) without detailed explanations in the dataset.
    - **Description:** Based on the examples reviewed, correct answers are marked with scores but without any explanations or justifications as to why they are deemed correct. While the scoring seems straightforward for someone with a physics background, providing contextual explanations or references for these correct answers might significantly enhance the dataset's educational value and reliability. It may mitigate the risk related to the hint hinting at potential misalignment of correct answers.

Without access to detailed subject matter expertise validation for each 'target_score' or incorrect/correct answer alignment, no evident discrepancies are observed from the examples inspected. Additional checks across the entire dataset or consulting with subject matter experts would be necessary to identify any specific issues correctly as pointed out in the hint.

Given the current context, the potential issues are speculative, based on data structure observations without asserting inaccuracies in the scientific content provided.