Bibliographic record
Abstract
Being a parent, my wallet has its share of children’s photos. Like all parents, I have carefully selected the most flattering ones for display. Similarly, as a scientist, I am apt to display only the most flattering snapshots of my efforts, that is, only those instances where data and theory coincide. In the scientific literature, rarely do we see data published without an accompanying explanation that places it within new or existing theories. There is reason for this. As scientists, we are uncomfortable with the presentation of uninterpretable data, as it implies a lack of understanding or a poorly conceived experiment. As readers, we do not want to see journal pages filled with “data reports” that provide little insight into hydrogeologic phenomena. Thus, the scientific literature is filled with snapshots of “beautiful babies.” I contend, however, that there is a vast number of data that do not fit neatly within available interpretations. Are we slowing the advancement of our science by not looking more closely at some of those “ugly babies”? (I am assuming that those “ugly babies” are the result of properly designed and monitored experiments.) Being involved in field-oriented research in fractured-rock aquifers, I have files and files of data. Because of the heterogeneity in fractured rock, hydraulic and chemical responses do not always fit within well-defined interpretations, and I would venture that the same is true for many complex physical, chemical, and biological processes in most geologic settings. In attempting to decipher data, I am faced with the question of whether I am observing an artifact of site-specific heterogeneity, or a yet uninterpreted phenomenon that may be transferable from site to site. The former may be of little interest in the hydrogeologic literature, whereas the latter potentially would be of widespread interest. Field-oriented research is extremely expensive, and there is a dearth of detailed data sets, at least for fractured-rock aquifers. Thus, rarely are there comparisons of data from multiple sites. Recently, I made an effort to examine and synthesize hydraulic and chemical experiments conducted at several fractured-rock sites. Most of the data I collected with collaborators, but I have also been drawing upon unpublished data from colleagues. I have been surprised to see dramatic similarities between these unpublished data, which has led to new insight into processes in fractured rock, insight that would have been lost without looking at data from multiple sites that were deemed to be uninterpretable. There have been huge benefits to the hydrogeologic community in the availability of data from the experimental sites at Camp Borden, Ontario (e.g., MacKay et al. 1986. Water Resources Research 22, no. 13: 2017–2029), Cape Cod, Massachusetts (e.g., LeBlanc et al. 1991. Water Resources Research 27, no. 5: 895–910), and the Macro-Dispersion Experiment (MADE) site in Alabama (Boggs et al. 1992. Water Resources Research 28, no. 12: 3281–3291). In fact, the data from the MADE site initially confounded many hypotheses, and these data have been a source for reevaluation, leading to advances in the understanding of geologic controls on chemical migration (e.g., Feehley et al. 2000. Water Resources Research 36, no. 9: 2501–2515). These sites are examples where huge investments have been made to test existing or new hypotheses; however, a wealth of data has been collected on much smaller scales that infrequently find their way into the accessible literature. Making those data available might result in insight that will accelerate our understanding of complex processes in geologic environments. A mechanism for publishing and sharing our “ugly babies” should be developed, but without filling journal pages as data reports. The Internet provides an available means of sharing information, and professional societies and publishers are embracing paperless publishing of scientific literature. With an eye toward advancing scientific understanding, professional societies and publishers could administer a separate series of paperless publications that are dedicated to the presentation of data that may not be readily interpreted by existing theories, or when synthesized with data from other sites may lead to new hydrogeologic insight. These data should be subject to some level of review prior to their posting to ensure explanations of data collection are thorough and metadata are complete. In addition, as scientists, we need to overcome the stigma of presenting uninterpretable data and feel no embarrassment in publishing our “ugly babies.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".