Explore prior work. Analyse the data from your study.

It can retrieve related papers, organise a corpus and prepare screening fields. SPECTER2 compares English scientific titles and abstracts; its proximity adapter suits searches starting from a reference paper. For a systematic review, retain the search strategy, inclusion criteria and team decisions: similarity does not guarantee an exhaustive search.

Prepare titles and abstracts and retain DOIs or other identifiers. For related-paper retrieval, combine allenai/specter2_base with the allenai/specter2 adapter following its documentation. Short-query search uses a different configuration. Share the model, adapter and dependencies for deployment compatibility review; a base checkpoint alone is not a complete integration.

Granite Docling can convert pages into structured text and tables. Your pipeline should retain the source paper, page and table and review headers, units, notes and values. Then define which findings are comparable by method and experimental conditions. Document conversion prepares the data; selection and normalisation depend on your study.

The linked variant works with multispectral, multitemporal imagery. Prepare bands, resolution, dates and normalisation according to its documentation; an RGB screenshot does not replace those inputs. For land-cover or change-detection tasks, use study labels and hold out distinct regions or periods to evaluate generalisation.

You can evaluate its visual features as a starting point for image comparison or an adapted classifier. The checkpoint does not include a validated detector for your instrument or measure cells or defects on its own. Use labelled samples and check changes in lighting, scale and equipment before incorporating it into analysis.

Your code prepares inputs, queries the model when deployment is compatible and collects outputs for analysis. Retain each paper, image or sample identifier. For models with adapters or specialist preprocessing, review those dependencies when defining the endpoint; the notebook coordinates the study’s different stages.

Fix a dataset version, record the checkpoint, adapters, preprocessing and parameters, and retain evaluation code. Compare all models on the same splits and against a baseline. Separate training and evaluation by study, instrument, region or date where appropriate to reduce information leakage.

Test a representative batch and measure time and memory per page, paper or image. Include resolution, number of dates and batch size in the estimate. Separate initial corpus processing from updates: retaining results and recomputing only changed inputs can reduce repeated work.

Store DOI, source, date and version with each record. When a publication is corrected or retracted, update its status and derived data and identify affected analyses. Your integration chooses which sources participate in each search and retains the selection used in an experiment for later review.