methods section
Definition: The report section that states how observations were obtained, calibrated, reduced, and analyzed.
Why it matters here: It lets the technical audience reproduce the path from raw files to measured quantities.
Relationships: It should connect instrument settings, calibration frames, pipeline steps, equations, and software choices in executable order.
Boundary / common confusion: A list of software names without parameters, inputs, and decisions is not a reproducible method.
results section
Definition: The report section that presents measurements and derived quantities with uncertainties, units, and relevant evidence.
Why it matters here: It is where calibrated outputs such as magnitude, SNR, velocity, or event depth are stated without hiding their statistical support.
Relationships: Each result should trace to a method and display the value, uncertainty, and conditions needed for interpretation.
Boundary / common confusion: Results should not silently mix interpretation with unsupported causal claims.
limitations section
Definition: The report section that identifies assumptions, systematics, incomplete coverage, and alternative explanations that bound the conclusion.
Why it matters here: It calibrates the reader’s trust and prevents the conclusion from outrunning the data.
Relationships: Cadence, comparison-star stability, calibration drift, sample selection, and model approximations are typical limitations linked to earlier analyses.
Boundary / common confusion: A limitation is not a generic disclaimer; it should state how the issue could change the result.
reduction pipeline
Definition: The ordered, parameterized sequence that converts raw observational files into calibrated measurements and derived products.
Why it matters here: It provides the backbone of the methods section and a reproducible chain for checking each claim.
Relationships: Inputs, calibration operations, quality checks, intermediate products, and final measurements should all be traceable through the pipeline.
Boundary / common confusion: Automation does not make a pipeline objective; undocumented defaults can encode major scientific choices.
reproducibility statement
Definition: A concise declaration of where the data, code, calibration products, query text, versions, and execution instructions needed to repeat the analysis can be found.
Why it matters here: It gives the reader a practical route from the written report to the underlying evidence.
Relationships: The statement completes provenance by naming persistent artifacts and the environment or versions required to regenerate results.
Boundary / common confusion: Saying “available on request” or naming only a repository without a version does not fully specify the analyzed state.
claim strength
Definition: The degree of confidence and specificity justified by the quality, uncertainty, and scope of the evidence.
Why it matters here: It governs whether the report should state a detection, a tentative indication, an upper limit, or no conclusion.
Relationships: Claim strength depends on effect size relative to uncertainty, replication, systematic checks, and model alternatives.
Boundary / common confusion: More decimal places or confident wording do not increase evidential strength.
technical audience
Definition: Readers who understand the field’s methods and need enough precision to evaluate or repeat the work.
Why it matters here: Writing for this audience requires explicit units, assumptions, calibration choices, uncertainty, and evidence rather than vague narrative.
Relationships: The report’s methods and results should use standard terminology while defining project-specific choices and data products.
Boundary / common confusion: Technical writing is not made stronger by unexplained jargon; precision includes making the reasoning auditable.