update links to concepts

scripts/resources/tooltips.json

@@ -8,6 +8,6 @@
   "composition_constraint": "Choose whether to include a composition constraint over two or more optimization variables such that their sum does not exceed a specified total (e.g. ensuring the mole fractions of elements in a composition sum to one). This constraint is particularly relevant to fabrication-related tasks where the quantities of components must sum to a total. Consider whether such a constraint reflects the reality of variable interactions when selecting this option.",
   "categorical": "Choose whether to include a categorical variable in the optimization process (e.g. dark or milk chocolate chips in a cookie recipe). Including categorical variables allows choice parameters and their interaction with continuous variables to be optimized. Note that adding categorical variables can create discontinuities in the search space that are difficult to optimize over. Consider the value of adding categorical variables to the optimization task when selecting this option.",
   "custom_threshold": "Choose whether to apply custom thresholds to objectives in a multi-objective optimization problem (e.g. a minimum acceptable strength requirement for a material). Setting a threshold on an objective guides the optimization algorithm to prioritize solutions that meet or exceed these criteria. Excluding thresholds enables greater exploration of the design space, but may produce sub-optimal solutions. Consider whether threshold values reflect the reality or expectations of your optimization task when selection this option.",
-  "synchrony": "Choose whether to perform <a href='curriculum/concepts/batch/SingleVsBatch_concept.md'>single or batch evaluations</a> for your Bayesian optimization campaign. Single evaluations analyze one candidate solution at a time, offering precise control and adaptability after each trial at the expense of more compute time. Batch evaluations, however, process several solutions in parallel, significantly reducing the number of optimization cycles but potentially diluting the specificity of adjustments. Batch evaluation is helpful in scenarios where it is advantageous to test several solutions simultaneously. Consider the nature of your evaluation tool when selecting between the two options.",
+  "synchrony": "Choose whether to perform <a href='curriculum/concepts/batch/single-vs-batch.md'>single or batch evaluations</a> for your Bayesian optimization campaign. Single evaluations analyze one candidate solution at a time, offering precise control and adaptability after each trial at the expense of more compute time. Batch evaluations, however, process several solutions in parallel, significantly reducing the number of optimization cycles but potentially diluting the specificity of adjustments. Batch evaluation is helpful in scenarios where it is advantageous to test several solutions simultaneously. Consider the nature of your evaluation tool when selecting between the two options.",
   "fidelity": "Choose whether to perform single or multi-fidelity optimization. Single-fidelity optimization uses a single evaluation method for all optimization trials, while multi-fidelity optimization leverages multiple evaluation methods with varying computational costs. Multi-fidelity optimization can be more efficient than single-fidelity optimization, as it uses cheaper evaluations to guide the optimization process. Consider the availability of different fidelity levels, their computational costs when selecting this option, and compatibility with other algorithms when making this choice."
 }