Check Claims Before You Publish

Top-line scientific read

Provided bibliometric snapshot (5 papers; total citations 35; h-index 2) indicates a small publication record concentrated in agronomy/soil/fertilizer applied-research topics. That pattern suggests an author producing domain-specific, probably locally relevant applied studies rather than high-citation, broad-basic-science work. Important caveat: "Li Wang" is a common name — identity disambiguation (ORCID, institution, email) is required before firm judgments about career-stage or research program can be made.

Data used for this review

• Author-provided publication list (5 papers) and summary metrics (h-index 2; citations 35; paper count 5).
• OpenAlex candidate-author search results (many high-profile "Li Wang" matches; highlights name ambiguity).

Recommendation: confirm which OpenAlex/ORCID identity corresponds to the author of the five listed papers before downstream meta-analytics or reputation inference.

Quantitative snapshot (from supplied data)

This visualization shows the raw supplied bibliometrics — small sample size and low citation counts consistent with early-career/applied-research profile or incomplete indexing.

Qualitative appraisal of publication list

The five paper titles provided are agriculture/agronomy/soil-and-fertilizer focused (walnut microbial fertilizer; maize-soybean intercropping lodging review; sustained-release potassium fertilizer with bentonite; weather analysis for Ganoderma cultivation; Chinese phosphorous fertilizer use/ direction). Patterns to note:

1. Topic focus: applied agronomy/soil/crop management — practical rather than molecular or theoretical biology.
2. Likely study types: field/greenhouse trials, formulation/engineering of fertilizers, cultivation-environment analyses, and narrative/systematic review (one review listed).
3. Potential strengths: practical relevance to local agriculture, applicability for growers, potential for experimental field data.
4. Potential weaknesses to check in methods: replication and randomization in field trials, statistical power and effect-size reporting, availability of raw data, detailed methods (fertilizer composition, controls), and potential confounding environmental variables.

Next step: obtain full-text PDFs and extract methods sections and raw data tables to judge experimental design and reproducibility.

Checklist for assessing scientific strength (actionable)

• Identity disambiguation: ORCID/institutional email — without it metric aggregation risks conflating multiple people with the same name.
• Methods reproducibility: are sample sizes, randomization, replication, statistical tests, and raw data reported?
• Data accessibility: are raw data, spreadsheets, or depositional links present or sharable on request?
• Statistical reporting: effect sizes, confidence intervals, multiple-testing corrections and full p-values rather than threshold statements.
• Study design suitability: for field agronomy, is there blocking/RCBD or split-plot design to account for field heterogeneity?
• Potential biases: funding sources, commercial fertilizer involvement, or lack of negative-results reporting.

If you want, I can fetch each paper's full-text, parse methods/results tables, and produce standardized evidence cards for each (sample size, design, key numerical outcomes, CI/SE, p-values) and replicate basic statistics where raw numbers exist.

Blindspots, biases, and limits of the current assessment

• Name ambiguity: "Li Wang" is extremely common — bibliometrics may refer to many different individuals; OpenAlex search returns many high-impact distinct researchers named Li Wang (big variance in h-index/citation counts). Disambiguation is essential before final judgments.
• Indexing gaps: applied-agronomy journals are sometimes under-indexed in global databases; citation counts may under-represent real-world applied impact.
• Small-n problem: 5 papers is a limited corpus to infer deep patterns — avoid overgeneralization.
• Potential local effect: agricultural studies may have high local utility (farmers/extension services) but low citation counts; that’s not equivalent to low scientific rigor automatically.

Concrete next actionable analyses I can run (pick one)

1. Identity disambiguation: search OpenAlex/WOS/Scopus/ORCID for Li Wang + paper titles/keywords to match the exact author profile (high priority).
2. Full-text methods extraction: parse each of the five papers for sample sizes, replication, statistical tests, and raw outcome tables; produce reproducibility evidence cards.
3. Citation-context analysis: retrieve citing papers and analyze whether citations are supportive/confirmatory or perfunctory.
4. Meta-evidence graph: create a Plotly network linking claims across the five papers (methods → results → conclusions) highlighting repeated methods/assays.
5. Generate a short critical reproducibility checklist tailored to each paper (power calc, design, randomization, blinding, raw-data availability).

Select one action and I will run it (I can start identity disambiguation immediately).