README.md

metadata

license: cc-by-4.0
pretty_name: CropMath
language:
  - en
  - zh
task_categories:
  - question-answering
tags:
  - agriculture
  - numerical-reasoning
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    default: true
    data_files:
      - split: dev
        path: data/default/dev.jsonl
      - split: test
        path: data/default/test.jsonl
      - split: gold
        path: data/default/gold.jsonl
  - config_name: eval_prompts
    data_files:
      - split: dev
        path: data/eval_prompts/dev.jsonl
      - split: test
        path: data/eval_prompts/test.jsonl
      - split: gold
        path: data/eval_prompts/gold.jsonl
  - config_name: hidden_public
    data_files:
      - split: hidden_public
        path: hidden_public/hidden_public.jsonl

CropMath

English | 简体中文

CropMath contains agricultural mechanistic formula questions with numeric reference answers and controlled knowledge conditions. The cropmath-v1 snapshot is publicly available at myy555/CropMath; the accompanying prompt builders, scorer and validators are in the code repository YuanyuanMa03/CropMath.

Contents and split relationships

Configuration	dev	test	gold	hidden_public
default	252	751	95	—
eval_prompts	1,512	4,506	570	—
hidden_public	—	—	—	160

The 95 gold questions are copied from test for auditing; do not add them to the test count or treat them as an independent split. There are 1,003 unique development/test questions. The six-condition expansion repeats the same questions with different knowledge blocks; it does not create independent questions. The formula catalog contains 62 public identifiers, CMF001–CMF062.

The public process categories are growth_yield, carbon_nitrogen_cycle, methane_emission, and environmental_response. Reasoning modes are direct, sensitivity, branch, and chain_2.

Loading from the Hub

from datasets import load_dataset

questions = load_dataset("myy555/CropMath", "default")
prompts = load_dataset("myy555/CropMath", "eval_prompts")
auxiliary = load_dataset("myy555/CropMath", "hidden_public")
assert len(questions["test"]) == 751
assert len(prompts["test"]) == 4506
assert len(auxiliary["hidden_public"]) == 160

Pin the released dataset revision in experiment records. Do not put access tokens in code or prediction files.

To work offline or to run the bundled validators, download this repository and use it as a local path. The standalone dataset directory also contains pyproject.toml, uv.lock, validation scripts and dataset tests. From its root:

uv sync --locked
uv run --locked pytest -q tests
uv run --locked python scripts/validate_cropmath_release.py --release-dir . --no-scan-repo-root
uv run --locked python scripts/validate_formula_catalog.py --path metadata/formula_catalog.csv

For the copy inside the GitHub repository, use that repository's root README commands instead. Validation success means the specified local checks passed; it does not establish scientific validity or online Viewer behavior.

The paper's full statistics tables (per-cell accuracy with cluster bootstrap 95% CIs, paired cluster tests with exact McNemar and FDR fields, thinking-mode control, parser hit rates, prompt-token statistics, K_wrong subset accuracies, tolerance robustness and behavior-distribution tables) are provided as CSV files under paper_stats/.

Fields and use

default rows include id, problem, formula, parameters, reasoning mode, knowledge blocks, per-condition prompts, answer and solution. eval_prompts rows include a condition-specific id, the original sample_id, condition, prompt, answer_float, precision and grouping metadata. Only send the selected prompt to a model. Do not include the answer, solution, other conditions' knowledge blocks or scoring metadata in the model input.

The six conditions are C (formula and inputs), K_name (name), K_formula (parameter semantics), K_domain (domain context), K_distractor (distractors), and K_wrong (an incorrect formula). The target remains the reference answer to the original task even when K_wrong supplies an incorrect formula.

Numeric scoring uses:

abs(prediction - answer) <= max(0.5 * 10**(-precision), abs(answer) * 0.01)

Unparseable predictions count as incorrect. Preserve sample pairing across conditions and account for formula-level clustering in statistical inference. See eval.yaml for the serialized protocol and HIDDEN_POLICY.md for auxiliary-set access.

Provenance and limitations

The snapshot contains standardized expressions, reference labels, a public formula catalog and audit metadata. It does not distribute private calculator implementations, generation code or a complete per-formula bibliographic mapping. Consequently, this package supports reusing questions and checking numeric predictions against stored labels, but does not independently reproduce the entire formula acquisition and reference-answer generation process.

The audit CSV and protocols are supplied records, not a new independent human audit performed by the package validator. Automated parser tests check the serialized solutions; they do not certify the human-audit provenance. Inputs are synthetic, and this benchmark does not establish reliability on field observations, complete crop simulations or real agricultural decisions.

The 160 auxiliary questions are public while their answers are withheld. No hosted scoring service is supplied. ID disjointness and withheld labels are not proof of absence of training contamination. See HIDDEN_POLICY.md.

Licensing and attribution

Dataset contents are provided under CC BY 4.0; see LICENSE. Validation code included in this dataset repository uses the MIT terms in CODE_LICENSE. The GitHub repository carries a separate MIT license for its code. Attribution metadata is provided in CITATION.cff, including the code repository link. No paper DOI is claimed. Existing notices are preserved. See reproducibility scope for what these artifacts support.