The research question is situated in a specific environmental and physiological context with relevant literature on ethanol runoff and Elodea proton‐extrusion.
Methodological considerations are well‐explained, linking control variables to their rationale and justifying replicates and CO₂ saturation.
Pre‐trial refinement and robust planning (e.g. simultaneous concentration runs) demonstrate thoughtful experimental design.
Justification for the choice of 0.2 M ethanol stock is missing, limiting contextual rationale.
pH meter calibration procedures and water sources (tap vs distilled) are not specified, impacting reproducibility.
Ambiguities in mass of Elodea (2 g vs 4 g) and light positioning details remain unresolved.
Data tables and graphs are clearly organized, with consistent units, significant figures, and error bars.
A suite of statistical tools (means, SD, Pearson correlation, t‐tests) is applied appropriately to address the research question.
Raw and processed data are separated and labeled, aiding transparency.
Uncertainty propagation is incomplete (systematic errors and combined uncertainties are not fully addressed).
Omissions in discussion of instrument bias (pH‐meter drift) and environmental uncertainties reduce precision.
Misinterpretation of correlation sign and lack of manual calculation steps indicate analytic inconsistencies.
Findings are clearly linked to key data points (peak effect at 4 %) and statistical outcomes.
External studies (Moroni, Marré) are effectively compared to justify experimental trends.
Internal inconsistencies (plateau vs enhancement statements) undermine full consistency with the analysis.
The negative Pearson r is misinterpreted as positive, reflecting confusion in data linkage.
Some mechanistic explanations lack clarity, reducing depth of justification.
Specific methodological limitations are explained with their likely impacts on variability and bias.
Recommended improvements are realistic, directly linked to identified weaknesses, and include explanations of how they enhance reliability.
The relative impact of each limitation is not quantitatively assessed, limiting depth of evaluation.
Some improvement suggestions lack technical detail (e.g. quantifying uniform light intensity requirements).
